A mechanically reconfigurable flexible frequency selective surface (FSS) with dual‐band and polarization selectivity is proposed based on buckling‐guided 3D assembly strategy. The structure consists of periodically arranged symmetric double split‐ring resonators (SRRs) in a bilayer configuration. By releasing the prestrain in a stretched substrate, a deterministic 3D architecture is formed, enabling continuous and reversible structural reconfiguration under biaxial tensile strain. The deformation modulates the coupling between incident electromagnetic waves and the resonant units, resulting in polarization‐selective transmission. For transverse magnetic wave, the structure exhibits a distinct dual‐stopband response, with resonant frequencies shifting toward lower frequencies as the tensile strain increases, while high transmission is maintained for transverse electric waves across the measured frequency range. The mechanical and electromagnetic models are employed to analyze the response mechanism, and the effects of the SRR radius and gap width on resonance tuning are discussed. The proposed structure features simple fabrication, stable performance under large deformation, and precise tunability, offering promising applications in flexible tunable filters and polarization‐selective electromagnetic devices.

As electronic devices continue to advance in performance and miniaturization, controllable dense successive droplet trains cooling represents an innovative solution for high-heat-flux applications. The present study achieves dense successive droplet trains generation by an in-house droplet generator with the maximum droplet flux of 2254,000 1/cm2s, reaching spray level. To facilitate active thermal regulation of the heated surface, micropillar surfaces (without cavities) and microtube surfaces (with cavities) are fabricated using photolithography and etching techniques. Compared to smooth surfaces, the critical heat flux (CHF) of successive droplets impacting micro-structured surfaces is enhanced by 34 %, the maximum heat transfer coefficient is improved by 180 %, the non-uniformity of temperature distribution reduced by 48 %, and the maximum heat flux exceeds 1000W/cm(2) at a volumetric flow rate of 4.5 cm(3)/s. At relatively low structural heights, microtube surfaces outperform conventional micropillar surfaces in heat transfer. Based on experimental results, the dimensionless correlation for the critical heat flux (CHF) of successive droplet trains impacting conventional micropillar surfaces is developed.

ABSTRACT Flexible multi‐parameter implantable sensors (FMPI‐sensors) are redefining the landscape of personalized medicine. By seamlessly integrating with living tissues, these soft electronic platforms enable real‐time, long‐term, and high‐fidelity monitoring of electrical, biochemical, mechanical, thermal, and optical signals across diverse organ systems. Unlike traditional implants that capture single signals, FMPI‐sensors promote decoding the complex interplay of cues underlying disease onset, progression, and therapeutic response. This review provides a comprehensive framework for understanding the design, application, and integration of FMPI‐sensors. We outline key advances in device architectures, multi‐signal fusion strategies, and intelligent interfaces across the brain, bones, internal organs, subcutaneous tissues, and vasculature. Special emphasis is placed on signal synergy, material innovation, and closed‐loop therapeutic potential. Finally, we discuss future challenges and opportunities in bioadaptive interfaces, autonomous power systems, real‐time AI inference, and regulatory frameworks. FMPI‐sensors are not merely tools for monitoring, they are evolving into intelligent, cooperative extensions of the human body.

Accurate and decoupled detection of normal and shear forces is essential for next‐generation tactile systems but remains challenging due to structural limitations and material constraints in existing flexible sensors. To address this, a biomimetic trilayer flexible sensor that integrates a rigid microcolumn and dual piezoresistive layers of liquid metal is designed, enabling simultaneous detection of pressure and shear strain. A scalable spray‐coating process is developed using ethanol‐ and iron‐modified liquid metal ink, which improves adhesion to PDMS and prevents nozzle corrosion. Guided by finite element simulations (ABAQUS controlled via Python), the sensor geometry is optimized for enhanced directional decoupling. Experimental results demonstrate excellent linearity ( R 2 > 0.996) across a wide pressure range (70.77–533.61 kPa), rapid response, and strong durability under repeated loading. This work provides a robust and scalable approach for fabricating high‐performance, multimodal flexible sensors with broad potential in robotic e‐skins, industrial inspection, and interactive electronics.

Wearable bioelectronic systems are rapidly emerging as a core technological platform for next-generation health monitoring, neuromodulation, and human-machine interaction. These applications impose stringent demands on materials, including softness, high electrical conductivity, conformal skin contact, and long-term durability. In this work, we present an ultrathin, highly customizable, and encapsulation-free smart textile platform based on a liquid metal (LM) and waterborne polyurethane composite. The conductive patterns are fabricated via laser pre-patterning and a one-step heat-transfer printing process, without the need for additional adhesives or encapsulation layers, enabling high-resolution integration with textile substrates. The resulting fabric electrodes exhibit outstanding initial conductivity, excellent mechanical stretchability, and robust wash durability over multiple laundering cycles. This textile platform supports a wide range of bioelectronic functionalities, including high-fidelity electrocardiogram (ECG) signal acquisition, rapid and tunable Joule heating for wearable warming applications, lightweight neural network-based gesture recognition (ten classes), and electrical stimulation for pain relief or functional therapy. Together, these results offer a unified, multifunctional platform for next-generation wearable devices and intelligent textile systems.

Fixed-topology sizing optimization of jacket structures must balance structural weight, static strength, and fatigue performance, but repeated static, pile-soil interaction, and deterministic fatigue analyses make multi-objective search computationally expensive. This study proposes a dual-source composite-surrogate-assisted constrained NSGA-II method. Structural weight W and maximum fatigue damage are minimized; is imposed as the explicit static constraint, and defines fatigue feasibility. Two heterogeneous composite surrogate configurations independently search the design space. Their candidates are merged, deduplicated, screened for representativeness, and subjected to a unified high-fidelity workflow comprising static analysis, pile-soil interaction processing, and Route-B deterministic fatigue assessment. Using reference-blind candidate selection, the method reduces the application-stage high-fidelity re-evaluation budget by 61.04% relative to the no-surrogate finite-sampling reference, while limiting the relative weight deviation of the lightest dual-constraint feasible design to 1.66%. The validated solutions capture the principal weight-fatigue-damage trade-off, although front coverage varies across the objective space. Results further show that fatigue damage, rather than static strength, governs the low-weight boundary of the representative jacket model. By directing expensive evaluations toward objective endpoints, fatigue-critical boundaries, and underrepresented trade-off regions, the proposed framework provides a reusable workflow for efficient lightweight and life-oriented sizing of fixed-topology jacket structures.

Biological soft tissues achieve a remarkable balance of compliance, toughness, and durability through their hierarchical fibrillar architectures, giving rise to nonlinear mechanical responses such as the characteristic J-shaped stress-strain curve. Inspired by these natural designs, periodic network metamaterials composed of curved beam elements have become promising candidates for soft robotics, bio-integrated electronics, and tissue engineering. This emergence underscores the need for a rigorous theoretical framework capable of guiding the rational design of biomimetic architectures and predicting their nonlinear mechanical behavior. Prevailing theoretical models, however, are typically formulated for specific, idealized geometries, limiting applicability to more general and complex network architectures and hindering accurate capture of multiscale deformation mechanisms essential for biomimetic performance. To overcome these limitations, a generalized nonlinear mechanical framework is presented in this study to characterize periodic networks of arbitrarily shaped curved beams, systematically capturing the hierarchical transmission of mechanical responses by bridging individual beam mechanics with lattice-level interactions under finite extension. The predictive capability is substantiated through finite element simulations and experimental validation across a wide spectrum of network configurations and loading conditions. Building upon this physically grounded framework, an efficient inverse design methodology enables precise tailoring of network architectures toward targeted mechanical specifications. In contrast to data-driven paradigms reliant on extensive training datasets or opaque neural architectures, the proposed approach combines computational efficiency with transparent physical interpretability. Collectively, this work provides a robust theoretical foundation and practical design paradigm for optimizing soft network metamaterials, offering a principled pathway for bio-inspired mechanical systems.

The development of multifunctional and high-performance polymer-based thermal interface materials is extremely challenging due to the interfacial thermal resistance arising from phonon scattering. Herein, a simple and effective strategy is proposed to construct polydimethylsiloxane (PDMS)-based composites (PHPL) with enhanced thermal conductivity using HKUST-1 as a three-dimensional skeleton. The results indicate that PHPL exhibits outstanding thermal performance, with a thermal conductivity of 1.47 W m- 1 K-1, which is 764.7 % higher than that of PDMS, and the maximum temperature change achieved was 20.4 degrees C. More importantly, the deicing efficiency of the PHPL composites was significantly increased by 107.1 %, while Young's modulus rose to 1.70 MPa. In addition, finite element simulation results revealed that the improvement in thermal conductivity can be attributed to the formation of a continuous thermal conductivity network within the polymer and the establishment of a high thermal conductivity pathway, thereby significantly reducing interfacial thermal resistance and facilitating effective heat transfer. This work not only provides new insights into the application of metal-organic frameworks, but also serves as a reference for the design and synthesis of efficient thermal interface composites.

The poor interfacial bonding in Cu/Mo immiscible alloys limits their applications. This study investigates the microstructure evolution and interdiffusion behavior of laser-cladded Cu/Mo coatings after heat treatment, using multiscale characterization and molecular dynamics simulations. The heat treatment drives oxide evolution from MoO2 and Cu2O (10 min) to CuO (20 min), and finally to dominant MoO2 (30 min). Simultaneously, it promotes a transition from columnar to equiaxed grains in the Mo layer, along with pore shrinkage and the creation of effective Cu diffusion pathways. Elemental analysis shows the interdiffusion distance increases from 0.67 μm to 1.63 μm, with a decelerating growth rate. These optimizations yield superior mechanical properties after 20 min: a 96.2% hardness enhancement, a reduced wear rate of 6.4×10−5 mm3·N−1·m−1, and a lower, more stable friction coefficient. Molecular dynamics simulations elucidate the atomic-scale diffusion mechanism, revealing a non-equilibrium process with an initial coefficient of 1.6×10−5 m2/s that decays to 7.74×10−6 m2/s, mediated by vacancies and dislocations. This work clarifies the non-equilibrium interdiffusion kinetics in the Cu/Mo system, providing a theoretical foundation for interface design in immiscible alloys.

With the integration and miniaturization of high-power electronic devices, developing thermal interface materials with high thermal conductivity is crucial to reducing the interfacial thermal resistance caused by fillers. Herein, a novel polymer-based thermal interface material (PDMS/ZZ@P/L) was reported, which was prepared using an in-situ growth method with ZnO/ZIF-8 heterostructure fillers. This material combined liquid metal and paraffin to fill the interface gaps and pores, forming a continuous thermally conductive network. Compared to PDMS, the thermal conductivity of the composites increased by approximately 929.4 %, and the temperature difference between PDMS/ZZ@P/L and PDMS under the same conditions reached 18.9 degrees C, while finite element simulations confirmed the enhanced thermal conductivity. In addition, the Young's modulus of PDMS/ZZ@P/L increased by approximately 1.7 %. More importantly, PDMS/ZZ@P/L exhibited excellent deicing performance, with the deicing time reduced by approximately 1.73 times. This work expands the research ideas for developing multifunctional thermal interface materials with practical applications.

This study investigates the wrinkle wavelength of film-substrate system in stretchable electronics, focusing on the shear deformation commonly overlooked in conventional models but critical for two-dimensional (2D) materials like graphene. Wrinkles are generated by releasing pre-strain in thin films bonded to compliant substrates, enabling stretchability for flexible electronic applications. Traditional theory treats films as continuum plates and ignores shear deformation, which becomes inaccurate for layered 2D materials bonded by weak van der Waals interactions, where interlayer shear deformation is significant. To address this limitation, an improved analytical model is developed based on Timoshenko beam theory to incorporate shear effects into the system's total free energy. The governing equation for critical wrinkle wavelength is derived via variational principles; due to its complexity, the perturbation method is used to obtain a third-order solution with sufficient accuracy. Theoretical predictions are validated against finite element analysis (FEA) with refined interface meshing, showing that wrinkle wavelength increases with film shear modulus and converges to traditional theory as shear modulus approaches infinity. The proposed model outperforms the conventional theory for low-shear-modulus films, thick films, and laminated structures. This work provides an accurate theoretical framework for analyzing wrinkling behaviors in film-substrate system, especially for 2D materials and layered structures where shear deformation cannot be neglected.

Accurate perception of spatial position is essential for both biological vision and intelligent unmanned systems. Existing radio-based positioning approaches are susceptible to interference and require bulky infrastructures, while optical systems often trade accuracy for compactness. Here, we present a compound meta-eye system (CMES) that integrates an array of metalens sub-eyes to capture angular parallax from multiple targets simultaneously. Each sub-eye focuses light onto a detector to form arrayed images, from which a global-ratio algorithm reconstructs spatial coordinates with high precision. The CMES enables multi-target positioning and motion tracking within a meter-scale range, achieving a relative depth error below 2% and trajectory-fitting deviations under 0.5 mm. The metalens design provides diffraction-limited focusing and wide angular tolerance, combining biological compound eye compactness with flat meta-optics. This compact optical-perception platform offers an efficient solution for real-time multi-target spatial perception, with potential applications in formation control, visual navigation, and environmental perception for unmanned aerial vehicles and embodied intelligent agents.

Understanding the characteristics of surface temperature uniformity and fluctuations in high heat flux cooling technology is crucial for achieving precise thermal management in high-power electronic devices. This study investigates the temperature non-uniformity (TNU) and temperature fluctuations (Delta Tb,tmax) in sparse and dense droplet train cooling, utilizing a self-designed droplet generator to achieve precise control over droplet flux and size. The droplet flux reached a maximum of N = 2,489,200 1/cm2s, and the minimum droplet diameter approached 150 mu m, nearly reaching spray cooling levels. Experimental results revealed that in the single-phase region, TNU does not exhibit a consistent decrease with increasing droplet train number, as interactions between adjacent droplet trains increase. Hot spots were observed in both the impact points and the liquid hump zones. In the nucleate boiling region, increased droplet train number improved temperature uniformity and reduced temperature fluctuations. Additionally, an adaptive arrangement of droplet trains, adjusted according to hot spot locations, enhanced cooling performance and critical heat flux (CHF), with the highest CHF reaching 923 W/cm2, compared to 860 W/cm2 for the standard configuration. This study demonstrates the significant potential of adaptive droplet train cooling in improving heat transfer uniformity, reducing thermal stress, and enhancing CHF in electronic cooling applications.

Sweat, a readily accessible bodily fluid, is an ideal medium for non-invasive detection and offers valuable physiological parameters for clinical diagnostics and health monitoring. Monitoring glucose levels in sweat is particularly significant for the diagnosis and management of diabetes, as well as for tracking the health status of diabetic patients and enabling closed-loop treatment strategies. However, challenges arise due to the presence of numerous interfering ions that complicate glucose monitoring in sweat. Additionally, the low concentration of glucose in sweat requires the use of traditional glucose sensors with immobilized enzymes to improve specificity and sensitivity, potentially leading to increased sensor costs. This study presents a novel approach of platinum-plating the surface of liquid metal to create a unique composite metal surface for a sensing electrode to solve these problems. The non-enzymatic glucose composite metal sensor, developed using in-situ platinum plating technology on liquid metal, enables specific recognition of glucose in sweat. This low-cost and flexible manufacturing process for sweat sensors does not require a complex production environment, thus opening up the possibility of inexpensive wearable personalized sweat monitoring.

Spray cooling exhibits outstanding cooling performances compared to other liquid cooling techniques, which offers robust thermal management for numerous applications facing high heat flux challenges. In spray cooling, coolant droplets generated from a spray nozzle continuously impinge onto a hot surface at high flow rates. The interaction between the droplets and the surface - whether they land on a pre-existing liquid film or directly on the heated area - depends on the fluid saturation temperature and the surface temperature. Understanding the dynamics and heat transfer during droplet impact is crucial for advancing spray cooling research. The present work summarizes the recent advancements in the study of droplet impact dynamics and heat transfer in spray cooling from two aspects. The first aspect is about the statistical analyses of droplet behaviors and liquid film conditions in spray cooling, examining their influence on cooling efficiency. The second one is regarding the droplet-surface interactions in spray cooling, ranging from single droplet to spray by increasing the complexity of droplet condition and surface condition. It includes the single droplet impacting a dry heated surface, multiple droplets impacting a dry heated surface, and droplets impacting the heated flowing film.

As electronic devices continue to shrink in size while demanding higher cooling performance, the need for miniaturized, controllable high-heat-flux cooling technologies has become increasingly urgent. This study proposes a novel controllable high-heat-flux droplet train cooling technology, utilizing a custom-designed droplet generator to achieve droplet fluxes (N) exceeding 2,000,000 1/cm2s over a 1 cm2 cooling area. Nucleate boiling heat transfer and critical heat flux (CHF) in controllable droplet trains cooling are experimentally investigated with varied ratios of droplet train spacing to droplet diameter S/D0, volumetric flow rates and subcooling degrees. The maximum Critical Heat Flux (CHF) is up to 1037 W/cm2 at the volumetric flow rate of 6.83 cm3/s. Based on experimental results, the heat flux correlations of nucleate boiling and CHF are proposed, with the dimensionless critical heat flux correlation indicating that logCHF*proportional to- (S/D0). The development of controllable droplet train cooling technology presents promising opportunities for enhancing high-heat-flux thermal management in compact electronic systems.

Electronic skins endow robots with sensory functions but often lack the multifunctionality of natural skin, such as switchable adhesion. Current smart adhesives based on elastomers have limited adhesion tunability, which hinders their effective use for both carrying heavy loads and performing dexterous manipulations. Here, we report a versatile, one-size-fits-all robotic adhesive skin using shape memory polymers with tunable rubber-to-glass phase transitions. The adhesion strength of our adhesive skin can be changed from minimal (~1 kilopascal) for sensing and handling ultralightweight objects to ultrastrong (>1 megapascal) for picking up and lifting heavy objects. Our versatile adhesive skin is expected to greatly enhance the ability of intelligent robots to interact with their environment.

This study presents bioinspired smart wings for micro flapping-wing robots, integrating stretchable electronics with Presprayed Galinstan Pin Extension (PGPE) technology. The smart wings incorporate real-time motion sensing and thermal management systems directly into flexible wing structures, addressing critical challenges in flight control, stability, and environmental adaptability. PGPE-enhanced liquid metal circuits provide superior electrical conductivity, adhesion, and mechanical resilience under dynamic deformation, ensuring stable performance during continuous flapping motion. Furthermore, the technology achieves enhanced passive thermal dissipation via an improved interface design, effectively mitigating overheating during prolonged operation. The circuits enable precise monitoring of wing angular position and acceleration, optimizing flight dynamics and improving system reliability across diverse conditions. Experimental results demonstrate the seamless integration of PGPE-enhanced circuits within the wing structure, maintaining stable electrical performance and effective thermal regulation under mechanical strain. By combining motion sensing and thermal control, these smart wings significantly enhance the functionality and adaptability of flapping-wing robotics, paving the way for advancements in environmental monitoring, search-and-rescue, and surveillance applications. This work highlights the potential of PGPE technology in revolutionizing flexible electronics for bioinspired robotic systems.