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Janus Nanozyme With Programmable Catalytic Switching for Adaptive Therapy of Diabetic Wounds
ABSTRACT Diabetic wound healing requires dynamic bidirectional regulation of reactive oxygen species (ROS). Herein, guided by density functional theory (DFT) calculations, we propose a programmable strategy to engineer catalytic pathways via an intimate heterointerface in Janus‐structured Cu‐Ag nanoparticles (CuAg‐J). Theoretical simulations reveal that the unique Cu‐Ag heterointerface induces a charge transfer to oxygen intermediates, enabling spontaneous formation of reactive species and pH‑switchable catalytic activity. Under acidic conditions, CuAg‐J exhibits peroxidase (POD)‐like activity ( K m = 0.19 mM, V max = 0.38 µM/s) for antibacterial ROS generation. Under neutral conditions, it displays superoxide dismutase (SOD)‐like and catalase (CAT)‐like activities ( K m = 9.48 mM, V max = 6.88 µM/s) for ROS scavenging and oxygen production. In an infected diabetic wound model, this bidirectional ROS regulation effectively breaks the oxidative stress–hypoxia–inflammation vicious cycle, significantly accelerating healing and achieving 92.04% wound closure by Day 14. This study not only presents a high‐performance nanozyme but also provides a new design rationale for engineering intelligent catalytic materials capable of autonomous function switching in response to dynamic microenvironmental conditions.
Triboelectric Tactile Sensors for Embodied Intelligence: Design, Performances, and Applications
ABSTRACT Embodied intelligence is driving artificial intelligence from digital reasoning toward real physical interaction, while tactile perception capabilities lag behind algorithmic advances and have become a critical bottleneck restricting the practical implementation of robotic closed‐loop interaction. We propose that triboelectric event‐driven tactile sensing can serve as a tactile information acquisition strategy for embodied intelligence. With its distinctive characteristics of mechanically triggered output and self‐powered operation, it provides a new route to overcoming the limitations of signal acquisition, transmission, and power supply in complex environments and under high‐density deployment. Based on this perspective, this review centers on triboelectric event‐driven tactile sensing and systematically discusses the implementation pathways of triboelectric tactile sensors for embodied intelligence from four aspects: the conversion mechanisms from tactile events to electrical signals, material and structural design, key deployment performance, and embodied interactive applications. Particular emphasis is placed on revealing the intrinsic relationships among design strategies, deployment capabilities, and scenario adaptability. Finally, this review further discusses the key challenges and future directions that must still be addressed for triboelectric event‐driven tactile sensing to advance from device‐level performance optimization toward an event‐driven system‐level perception paradigm.
Multiscale Ion Transport Optimization in High‐Mass‐Loading Cathodes for Aqueous Zinc‐Ion Batteries
ABSTRACT High‐mass‐loading cathodes are essential for translating aqueous zinc‐ion batteries (AZIBs) from laboratory demonstrations to practically relevant energy‐storage devices because they increase the fraction of electrochemically active materials and thereby improve device‐level energy density. However, increasing cathode loading and thickness simultaneously aggravates charge transport limitations, electrolyte infiltration insufficiency, and structural degradation, leading to severe polarization, poor active‐material utilization, and rapid performance decay. In this review, we present a multiscale framework for understanding and optimizing high‐mass‐loading AZIB cathodes, spanning the microscale, mesoscale, and macroscale. At the microscale, we discussed how doping, vacancy, and interlayer‐spacing modulation regulate electronic structure, Zn 2+ diffusion, and structural stability of active materials. At the mesoscale, we examine pore architecture, cathode components, and fabrication strategies, with emphasis on porosity, tortuosity, electrolyte accessibility, and integrated charge transport in thick cathodes. At the macroscale, we highlight the importance of electrode balancing and electrolyte management for translating material‐level advances into practical cell performance. We further outline emerging directions, including operando characterization, cross‐scale modeling, data‐driven materials discovery, and scalable manufacturing. By connecting material chemistry, electrode architecture, and device integration, this review provides a coherent roadmap for the rational design of high‐mass‐loading cathodes and the development of energy‐dense, commercially relevant AZIB systems.
Elastomeric 3D‐Printed Microenvironments Enable Nanonewton Force Measurements in Healthy and Diseased Human Pluripotent Stem Cell‐Derived Neuroepithelial Cells
ABSTRACT During neural development, cells generate and respond to mechanical forces within their microenvironment while they migrate and differentiate. These forces are central to the development of the human brain, as they are transduced into intracellular signals that regulate key neurobiological processes and may be involved in pathological processes. However, measurement of such forces in the nanonewton (nN) range within a three‐dimensional (3D) microenvironment remains technically challenging and computationally intensive. Here, we present a solution to this challenge, based on elastomeric 3D microstructures fabricated via two‐photon polymerization (2PP) and a tailored wet‐etching process. The resulting free‐standing beam architectures enable the quantification of nN forces exerted by pluripotent stem cell‐derived neuroepithelial progenitor cells. We characterized the morphology of the cells and the forces they exerted using live‐cell confocal imaging microscopy, scanning electron microscopy (SEM), supported by an in‐house routine for intensity‐based deflection measurements with sub‐pixel resolution, and atomic force microscopy (AFM). Using this platform, we precisely quantified neuronal traction forces down to 1.2 nN from both healthy neural cells and those carrying a TSC2 gene mutation linked to tuberous sclerosis complex (TSC). The proposed elastomeric microenvironment paves the way for investigating cytoskeletal mechanics, neuromechanobiology in 3D, and for developing in vitro disease treatment models.
Stress Transfer Within Microphase‐Separated Structures: A Post‐Synthetic Strategy to Impart Mechanoresponsiveness to Block Copolymer Materials
ABSTRACT Mechanoresponsive polymers, which exhibit changes in properties such as color in response to mechanical stimuli, have attracted increasing attention for smart materials. Among various design strategies, mechanophore‐incorporated systems are widely used approaches that enable precise molecular‐level design and control; however, this approach often suffers from complicated synthesis and limited applicability to existing materials such as block copolymers, which have found widespread industrial use as high‐performance materials. Here, we present a post‐synthetic strategy enabled by exploiting microphase‐separated structures to impart mechanochromism to existing polymer systems while retaining the advantages of mechanophore‐based approaches. By exploiting the microphase‐separated structure of styrene–butadiene–styrene (SBS) block copolymers, blending with polystyrene bearing a tetraarylsuccinonitrile (TASN) mechanophore enables domain‐selective localization of the mechanophore within the rigid domains without chemical modification of the host polymer. The resulting materials exhibit fluorescence upon tensile deformation originating from force‐induced TASN cleavage, demonstrating mechanoactivation even under minimal deformation of the hard domains. The mechanoresponse is governed by the molecular weight and content of the mechanophore‐containing polystyrene. Importantly, bulk mechanical properties are preserved and can even be enhanced. This strategy establishes a scalable platform for integrating mechanochemical functionality into existing polymer systems.
Entropy Modulation Boosted Ultra–Broadband Electromagnetic Wave Absorption of Si(Zr,Hf,Ta,Ti,Nb)BCN Ceramics
ABSTRACT SiMBCN ceramics containing one or multiple metallic elements are synthesized via chemical modification of polyborosilazane using one to five metal alkylamino compounds with an equimolar ratio (i.e., M = Zr, Hf, Ta, Ti, and/or Nb). The effects of the number and types of metallic elements on the local lattice distortion, microstructure, dielectric properties, and electromagnetic wave absorption (EMA) performance are systematically investigated via experiments and ab initio calculations. These results show that increasing the number of metallic elements remarkably increases the local lattice distortion and suppresses the crystallization of the ceramics, which strongly affects the dielectric properties, showing a significant entropy effect. Particularly, the SiZrHfTaTiNbBCN ceramics remain in nearly amorphous state even after annealing at 1600°C for 5 h. Under the synergistic effect of annealing temperature and entropy engineering, the SiZrHfNbBCN ceramic develops a unique microstructure consisting of a ZrHfNbCN/Si 3 N 4 /C free core with abundant interfaces and a low‐dielectric β‐Si 3 N 4 ‐based shell. This microstructure balances the impedance matching and dielectric loss, resulting in an ultra‐broad effective absorption bandwidth at 1.90 mm (7.65 GHz), which is superior to all previously reported polymer‐derived ceramics (PDCs) at thickness < 2 mm and therefore presents a significant advance in ceramic‐based EMA materials for high‐temperature applications.
Organic n‐Type Molecule Mediated Crystallization Regulation for Sn‐Pb Perovskite Solar Cells
ABSTRACT Narrow‐bandgap Sn‐Pb perovskites have showcased great potential for constructing tandem perovskite solar cells (PSCs). However, Sn 2 + in Sn‐Pb PSCs is readily oxidized to Sn 4 + , and such B‐site Sn 4 + will introduce excess positive charge and causes severe p‐type self‐doping, which pins the Fermi level and aggravates non‐radiative recombination. Additionally, the Sn‐based component crystallizes far more rapidly than that of Pb, resulting in unbalanced Sn‐Pb crystallization. To address these challenges, we develop a novel n‐type molecule NDBH as perovskite additive. This molecule offers three key advantages: prevents the oxidation of Sn 2 + through chelation and blocks oxygen ingress along grain boundaries; balances the crystallization rates of Sn‐Pb perovskite by selectively chelating Sn 2 + ; modifies the perovskite surface to be more n‐type. With this organic n‐type molecule, the inverted Sn‐Pb PSCs achieve power conversion efficiency (PCE) of 24.11%. The devices also exhibited remarkable long‐term stability, retaining over 90% of their initial efficiency after 600 h of maximum power point (MPP) tracking. This approach of designing multifunctional n‐type molecules offers a new perspective for enhancing the efficiency and stability of Sn‐Pb PSCs.
Data‐Driven Materials Science for Energy‐Sustainable Applications
ABSTRACT Materials science is underpinned by structure‐property relationships that govern the function of a material. These relationships can be encoded into algorithms and integrated into machine‐learning models that enable the prediction of materials and their cognate properties. However, machine‐learning models are largely being trained on computed data owing to a worldwide shortage of real‐world (experimental) datasets. This review describes how to capture and collate experimental data from scientific literature using artificial‐intelligence (AI) methods to produce materials‐domain‐specific datasets or language models. Their application in AI‐driven enquiries that facilitate progress in energy‐sustainable materials science is then illustrated via six case studies that cover: training machine‐learning models, data‐driven materials discovery, optimizing manufacturing processes, mapping phases of materials, forecasting materials‐centric research trends, and classifying types of materials using automated prompt engineering. The future of materials‐domain‐specific datasets, language models, and decision‐making workflows using AI agents is then envisioned for the energy sector. The intrinsic challenges of accessing historical dark data in materials science are then described and contrasted with timely opportunities for leveraging massive amounts of experimental data from laboratories in going forwards; by exploiting electronic‐lab notebooks, high‐throughput experiments, and digital‐twin technologies. These opportunities are illustrated for energy‐sustainable materials science, especially the photovoltaic and battery industries.
Lignin Mimicking Protein Methylation Augments the Efficacy of FOLFOX Chemotherapy by Anchoring Thymidylate Synthase
ABSTRACT FOLFOX has served as the standard chemotherapy regimen for advanced stages, specifically in the treatment of pancreatic, colorectal, and bladder cancers. However, the issues of excessive toxicity and prolonged treatment cycles persist. To address this limitation, we developed a novel methylated amino acid‐modified lignin inspired by biological methylation, creating a pH‐responsive charge‐reversal drug delivery system for FOLFOX chemotherapy. This system stabilizes thymidylate synthase (TS) and combines with deoxyuridine monophosphate (dUMP) to demonstrate significantly improved therapeutic outcomes of FOLFOX against colorectal cancer (CRC). The FOLFOX‐loaded nanodrug showed targeted antitumor activity against CRC and induced tumor cell death through multiple mechanisms, including apoptosis, necrosis, cell cycle regulation, DNA damage, and reactive oxygen species generation. Furthermore, the nano‐formulation activated immune responses to enhance chemotherapeutic potency and effectively reducing renal toxicity associated with chemotherapy drugs. These findings suggest that protein methylation‐mimicked lignin could be a valuable addition to current first‐line chemotherapy options and holds promise for enhancing chemotherapy against solid tumors.
Engineered Chiroplasmonic Nanointerfaces Enable High‐Dissymmetry Circularly Polarized Electrochemiluminescence From Achiral Luminophores
ABSTRACT Circularly polarized electrochemiluminescence (CP‐ECL) provides an emerging route to encode chiral information into electrochemically generated light, but current strategies largely rely on intrinsically chiral luminophores, limiting molecular diversity, device integration, and dissymmetry‐factor amplification. Here, a strategy based on engineered chiroplasmonic nanointerfaces is developed for high‐dissymmetry CP‐ECL from achiral luminophores. In this design, luminophores are spatially confined around helicoid Au nanocrystals within a nanoscale plasmonic environment, and the resulting hybrid nanocrystals are assembled into monolayer films to couple chiroplasmonic near fields with electrochemically generated excited states. Using Ru(bpy) 3 2+ as a model achiral ECL luminophore, the helicoid Au@SiO 2 ‐Ru nanocrystal monolayer electrode produces mirror‐image CP‐ECL responses with a high dissymmetry factor of |g CP‐ECL | ≈ 1.1. Control experiments and electromagnetic simulations reveal a synergistic mechanism in which chiroplasmonic near‐field induction predominantly breaks the emission symmetry, whereas circular‐polarization‐dependent extinction further amplifies the far‐field circular polarization contrast. Extension to another achiral ECL luminophore suggests that this strategy may be applicable beyond the Ru(bpy) 3 2+ system. By enabling circular polarization without requiring intrinsically chiral luminophores, this approach can be readily integrated with established achiral ECL chemistries and devices, offering opportunities for chiral sensing, electrochemiluminescent photonics, and optical information encoding.
Nuclear Pore Mechanotransduction in Oncology: A Structural Axis of Vulnerability for Targeted Intervention
ABSTRACT Nuclear pore complexes (NPCs) are massive protein assemblies that gate the nuclear envelope to regulate nucleocytoplasmic transport. Beyond their canonical role as conduits, NPCs act as mechanosensitive regulators that translate cytoskeletal, extracellular, and lamina‐derived forces into transport decisions coupled to cellular state. In malignancy, NPCs undergo extensive remodeling—marked by protein overexpression, hyperpermeability, and compromised scaffold integrity. While these alterations drive invasion and transcriptional plasticity, they simultaneously expose a lethal mechanical vulnerability; transformed cells become critically dependent on this altered NPC state. We propose the “nuclear mechanostat” as a testable conceptual framework in which NPC barrier stringency and nuclear mechanics are reciprocally coupled. Through the lenses of dilation, elastic deformation, and structural memory, we synthesize existing evidence and develop the hypothesis that pharmacological disruption of the permeability barrier or scaffold architecture erodes nuclear resilience. Because cancer cells operate near a mechanical failure threshold, targeting these conserved mechanotransducers reveals a pan‐cancer axis of vulnerability, opening new frontiers for precision oncology.
Ultrathin 2D Multivariate Hydrogen‐Bonded Organic Framework Nanosheets for Fluorescent DNA Sensing
ABSTRACT The multivariate strategy is generally incompatible with densely packed structures, as this approach typically requires porous frameworks to manage the steric demands of incorporated functional groups. Without sufficient free volume, the frameworks are prone to structural transformations driven by steric hindrance. Here, we demonstrate that this limitation can be overcome by exploiting an interaction hierarchy in hydrogen‐bonded organic frameworks (HOFs), where strong directional charge‐assisted hydrogen bonds define the framework while weaker interactions accommodate steric perturbations. This enables the extension of the multivariate strategy to densely packed systems, yielding multivariate HOFs with well‐defined structures and tailored functionalities. Specifically, a carboxylate‐amidinium‐based layered HOF (HOF‐FAFU‐4) was synthesized as a prototype platform. It enables the integration of diverse building blocks and precise tuning of the spatial arrangement of functional groups. Benefiting from their layered structure, these materials can be further exfoliated into ultrathin 2D nanosheets, thereby enhancing the accessibility of functional sites. These ultrathin 2D nanosheets serve as fluorescent sensing platforms for DNA detection, achieving a limit of detection (LOD) of 8.1 pM and demonstrating broad generality for multiple DNA analytes. This work not only broadens the scope of the multivariate strategy but also expands the structural diversity and applicability of HOFs.
Nonorthogonal Polarization‐Multiplexed and Detachable Diffractive Processors
ABSTRACT Polarization multiplexing provides an elegant route for parallel all‐optical processing, yet its intrinsic orthogonality fundamentally limits channel capacity and interlayer interaction in diffractive systems. We present a detachable diffractive processor that leverages nonorthogonal polarization multiplexing to overcome these bottlenecks. The processor features a convolutional‐like architecture composed of detachable diffractive layers, enabling global and interlayer function multiplexing with accelerated training and expanded versatility. Experimentally, we realize 12 independent functions—4 classifiers and 8 holograms—across 4 polarization‐decoupled channels in a dual‐layer system and extend to 18 channels supporting 6 classifiers and 36 holograms using a triple‐layer configuration. Furthermore, we demonstrate a multi‐level optical encryption framework that combines image transformation and holographic generation. This work establishes a scalable framework for multifunctional diffractive computing, enabling compact processors, high‐capacity holography, and secure optical information technologies.
Functional integrity of mesolimbic-hippocampal circuits is associated with anhedonia in individuals with early life stress
Anhedonia reflects a transdiagnostic deficit in a range of processes that impact reward and motivation. While human neuroimaging has mainly focused on striatal-related alterations in anhedonia, animal models suggest hippocampal [HPC] novelty processing regulates mesolimbic dopamine activity, implicating mesolimbic-HPC alterations in anhedonia. Childhood trauma, which disproportionately impacts HPC structure and function, may exacerbate this vulnerability. The present study of 37 males and 55 females examined whether HPC alterations interact with childhood trauma to predict anhedonia in humans. Using fMRI in a sample enriched for anhedonia, we assessed three HPC-related processes: resting-state connectivity with mesolimbic targets in the ventral tegmental area [VTA] and nucleus accumbens [NAc], task-based HPC novelty response, and task-based HPC modulation of VTA activation reflecting novelty-evoked facilitation of target detection. Significant interactions emerged for anticipatory anhedonia: reduced HPC-NAc connectivity, reduced novelty response, and weaker HPC→VTA modulation were each associated with greater anticipatory anhedonia among individuals with high childhood trauma. Moreover, LASSO regression confirmed these interactions as unique predictors. These findings suggest that early life adversity interacts with alterations in HPC-mesolimbic signaling to contribute to individual differences in anhedonia, highlighting the HPC as a potential target of motivation-related deficits in striatum. Significance Statement The ability to process rewarding stimuli and initiate goal-directed behavior is critical for human functioning. While human neuroimaging has highlighted the role of striatal function in these processes, animal evidence suggests that hippocampal modulation of mesolimbic dopamine signaling is critical for motivated behavior. The present study translates this framework to humans, demonstrating that hippocampal-mesolimbic functional integrity interacts with childhood trauma to predict individual differences in anticipatory anhedonia (i.e., impairments in one's ability to anticipate and pursue rewards). These findings implicate the hippocampus as a potential upstream contributor to motivational deficits and highlight early life stress as a key context in which mesolimbic circuit dysfunction becomes behaviorally relevant.
Achieving low write error rates in perpendicular spin–orbit torque MRAM: A micromagnetic modeling feasibility study
We report a micromagnetic modeling study of switching perpendicularly magnetized free layers with spin–orbit torques (SOTs) generated by spin currents consisting of only orthogonal (conventional SOT) as well as both orthogonal and collinear (unconventional SOT) spin polarizations relative to the free layer (FL) magnetization. Focusing on the FL diameter of 50 nm, for conventional SOT, we find relatively high probabilities of FL back-hopping or incomplete switching at room temperature (T) for several bias configurations that deterministically switch the FL at T = 0 K, including in-plane bias field with and without Dzyaloshinskii–Moriya interaction (DMI) as well as the field-free case with DMI in the presence of field-like torque. For unconventional SOT, fast, field-free deterministic switching is found at T = 0 K for orthogonal y-spin SOT efficiency θy = 0.3 when collinear z-spin efficiency θz/θy &gt; 0.25. Such large ratio is needed to change the nature of the magnetization switching from a non-uniform state with dominant component along y to an almost fully reversed and uniform state along -z, resulting in thermally robust and complete switching without back-hopping. In addition, the presence of z spins of such magnitude results in lowering of the switching current density by a factor of about 3, depending on the damping parameter α of the FL (more for lower α). Based on these results, we conclude that achieving low write error rate operation in perpendicular SOT MRAM is not feasible using approaches that exploit conventional SOT, but it should be straightforward to achieve with unconventional SOTs that meet parameter values determined by this study.
A method for enhancing the temperature measurement range of fiber specklegram sensors based on the integrated speckle intensity probability density function
Fiber specklegram sensors based on correlation demodulation suffer from rapid loss of correlation when the measured specklegram deviates too far from the reference image, causing their dynamic range to be limited by the saturation of the correlation coefficient. This restricts their application in wide-range temperature sensing. To overcome this limitation, we propose a novel demodulation method based on the integrated speckle light intensity probability density function, adopting the probability value of a specific light intensity interval as the characteristic parameter to enhance the dynamic range of fiber specklegram sensors. This paper elucidates the fundamental mechanism by which ambient temperature variations induce changes in the fiber length, diameter, and refractive index, subsequently leading to variations in the transmission modes and ultimately affecting the parameters of the integrated speckle light intensity probability density function. Theoretical derivation establishes the relationship between the probability value of a specific light intensity interval and the temperature change, thereby clarifying the principle of the proposed method for temperature sensing. A single-mode–no-core–multimode fiber structure is used as the sensing unit to perform principle verification and comparative experiments. The experimental results indicate that, within the operational temperature range of the experimental setup, our method expands the usable temperature range from 60–130 to 60–170 °C compared to the standard zero-mean-normalized cross correlation approach, achieving a 57.14% increase in the dynamic range. A slight reduction in linearity is observed and discussed, but the overall performance confirms that the proposed method substantially enhances the temperature measurement capability of fiber specklegram sensors.
Calculation of backscattering correction factors in AES for compounds: Unraveling matrix effects
Backscattering correction factor (BCF) data are essential for quantitative Auger electron spectroscopy (AES) analysis. This study computes BCFs for nine oxides and three zinc chalcogenides by using a Monte Carlo method that incorporates dielectric function theory for discrete inelastic events and Mott scattering cross section for elastic interactions. Results demonstrate that the BCF is highly sensitive to the mean atomic number of the matrix. Comparative analysis with NIST data reveals that the continuous slowing down approximation (CSDA) based model systematically underestimates the BCFs for heavy metal oxides, such as HfO2 and ZrO2. In these high-Z matrices, intense large-angle elastic scattering spatially distributes electron trajectories to be close to the surface region and thereby substantially increasing BCF. While our calculations align perfectly with NIST values for low-Z chalcogenides, the pronounced discrepancies in the heavy oxides definitively delineate the physical limits of the CSDA approach. Ultimately, this work provides a refined theoretical basis for eliminating systematic uncertainties in high-precision quantitative surface analysis by AES.
Thermodynamically admissible neural solvers for stiff electro-elastodynamics via exact geometric constraints
Physics-Informed Neural Networks (PINNs) serve as continuous, mesh-free solvers for partial differential equations, but they frequently encounter optimization failures when applied to strongly coupled, stiff multiphysics systems. In piezoelectricity, the disparity in energetic scales between mechanical stress and electric displacement creates severely ill-conditioned loss landscapes, resulting in gradient pathologies. Standard PINN formulations rely on penalty-based soft constraints for boundary and initial conditions, which exacerbate this stiffness, leak unphysical energy, and corrupt boundary stress calculations. In this work, we present a constrained PINN architecture for 1D coupled electro-elastodynamics that structurally bypasses this gradient competition. By applying the quasi-static approximation and utilizing analytical distance functions, we enforce exact Dirichlet boundaries and second-order time initial conditions directly within the neural network’s topology. This geometric constraint restricts the optimizer to a physically consistent energy manifold and enables recovery of maximal boundary stresses without internal gradient noise, a capability that is critical for predicting mechanical fatigue and failure in high-frequency transducer architectures. Stabilized by a deterministic quasi-Newton optimization stage, our fully constrained model resolves the mechanical displacement and electric potential fields with a global relative L2 error of O(10−5). These results demonstrate that the gradient pathologies typically observed in stiff multiphysics PINNs can be systematically neutralized through the exact algebraic imposition of boundary and initial constraints.
Thermoelastic dynamics of a nanoscale metallic film: Role of bilayer coupling revealed by extreme ultraviolet transient gratings
We investigate the thermoelastic dynamics of an 80 nm titanium thin film supported by a Si3N4 membrane by extreme-ultraviolet transient-grating (EUV-TG) spectroscopy across varying excitation fluences. A dominant oscillatory response at 143.8–150.6 GHz from time-domain fits is identified as a guided Lamb wave of the Ti/Si3N4 bilayer membrane with an in-plane phase velocity of approximately 6.3 km s−1. The near-coincidence of the Ti longitudinal and Si3N4 transverse acoustic velocities (∼6.1 km s−1) means that the dominant guided mode phase velocity simultaneously approximates both—a direct fingerprint of strong mechanical coupling between the layers. With increasing fluence, a systematic phase velocity increase of 4.7% is observed. A lower-frequency feature near 20–25 GHz is also detected, consistent with a breathing-type thickness response driven by transient uniform stress. Thermal relaxation analysis yields effective in-plane thermal diffusivities of (1.3–2.7) × 10−6 m2 s−1 and thermal conductivities of approximately 3–7 W m−1 K−1, significantly reduced relative to bulk Ti and consistent with boundary-limited heat transport in the confined membrane geometry. By combining nanometer-scale spatial selectivity with picosecond temporal resolution, EUV-TG enables simultaneous access to guided elastic wave propagation and nanoscale in-plane thermal transport in metallic membranes under non-equilibrium excitation. These results demonstrate how nanoscale confinement and multilayer coupling modify thermoelastic dynamics relative to bulk materials and highlight EUV-TG as a powerful tool for probing ultrafast thermoelastic dynamics in thin metallic films.
Quantifying the Correlation Between Capacity Utilization and Electrolyte Dosage for Ultrahigh‐Energy‐Density 769 Wh Kg <sup>−1</sup> Rechargeable Lithium Metal Batteries
ABSTRACT The pursuit of high‐energy‐density lithium metal batteries requires simultaneous optimization of electrode architecture, electrolyte formulation, and interfacial stability. Here, we establish a fundamental parameter g(σ e , D e ) that quantifies the relationship between electrolyte dosage and capacity utilization in ultra‐thick electrodes (>100.0 µm), enabling precise determination of the minimal electrolyte requirement (1.1 g Ah −1 ). Through systematic investigation of electrolyte compatibility with high‐loading cathodes (> 10.0 mAh cm −2 ) at high voltages (4.8 V), we develop an optimized formulation that forms stable interfaces while suppressing parasitic reactions. By integrating these advances—including a lightweight lithium metal anode—we demonstrate a 54.2 Ah pouch cell achieving 769 Wh kg −1 , representing a 150% improvement over conventional lithium‐ion batteries. This work provides both theoretical and practical frameworks for engineering next‐generation batteries through electrolyte minimization and interface stabilization.