Kenan Song

Fabricating thin, fully dense oxide ceramic electrolytes without polymer binders remains a central manufacturing bottleneck for all‐solid‐state lithium batteries (ASSBs). Conventional pressing routes yield (LLZTO) pellets exceeding 500 m in thickness, and the 15 –30 wt.% polymer binders require deposit grain‐boundary carbon during sintering, suppressing ionic conductivity by 10% –40%. Here, we demonstrate binder‐free direct ink writing (DIW) of LLZTO using a ternary ‐terpineol/ethylene glycol/ceramic capillary suspension, in which trace ethylene glycol (2 vol.%) generates a percolating pendular‐bridge network that imparts a recoverable yield stress of Pa and Pa without any polymer. The optimized 65 vol.% (85.9 wt.%) LLZTO ink exhibits pronounced shear‐thinning behavior. Sintering at 1300/4 h yields 95.8% relative density at 254 m thickness with an intrinsic bulk ionic conductivity of 17.3 and a cycle‐median bulk value of 11.8 mS , consistent with the upper range of Ta‐stabilized cubic LLZO.

A bioinspired three-channel coaxial dry-jet wet-spinning platform enables continuous TPU/PLA multilayer vascular grafts with tunable mechanics, a collagen-functionalized lumen, and favorable preliminary vascular-cell and blood compatibility.

Advanced structural and materials design increasingly depends on complex geometries and microstructures spanning continuum and atomistic descriptions. Yet the early idea–design–simulation loop remains manual: engineers translate informal intent into modeling assumptions, select physics, assemble solver inputs, and revise models as requirements evolve. Existing AI‐assisted design methods typically address well‐parameterized tasks within predefined spaces, offering limited support for this open‐ended exploratory phase. Here, we introduce an objectives and key results (OKR)‐agent, a coordinated peer multiagent framework that uses large language models (LLMs) to participate directly in mechanics design and simulation workflows. OKR‐agent organizes work around explicit OKR that encodes intent, task decomposition, and progress in a compact shared state. A supervisor, modeler, and simulator act as peers over an OKR board, coordinating reasoning with external continuum and atomistic solvers through a standardized tool set. This design keeps prompts subtask‐focused, limits context growth, and makes session evolution auditable. Across fiber‐bundle and polycrystalline‐metal molecular dynamics (MD) cases and plate‐with‐holes finite element method (FEM) benchmarks, the same team converts informal goals into executable simulations and interpretable outputs while accommodating mid‐session changes to geometry, boundary conditions, loading protocols, and microstructural architecture. A limitation remains: domain expertise is essential for validation and physical adequacy.

Inkjet printing enables scalable fabrication of nanomaterial-based devices, but stable droplet formation and reproducible deposition remain challenging for emerging nanosheet inks. In this work, we develop a model-guided inkjet printing framework that monitors and optimizes droplet generation based on fluid properties and nozzle dynamics. Unlike previous MXene inkjet printing studies that primarily rely on empirical parameter tuning and focus on ink formulation or device performance, the proposed framework employs a physics-based analytical model that quantitatively links waveform parameters, fluid properties, nozzle dynamics, and droplet behavior. This predictive approach enables rational waveform design for stable single-droplet ejection, minimizes satellite droplet formation, and reduces the need for extensive experimental calibration. MXene (Ti3C2Tx) nanosheets dispersed in ethanol are used as a representative conductive ink to demonstrate the capability of the optimized printing approach. Under model-guided printing conditions, the system achieves reliable pattern formation and enables fabrication of printed MXene devices that exhibit measurable responses to several gases relevant to agricultural environments. These results demonstrate that analytical-model-guided monitoring and optimization of the inkjet printing process provides a predictive and generalizable strategy for reproducible printing of nanomaterial-based devices.

While the photoluminescence of (Et ₂ NH ₂ ) ₃ SbCl ₆ is diminished by thermal quenching and FRET, Sb ³⁺ doping of the (Et ₂ NH ₂ ) ₃ InCl ₆ phase performed by three distinct approaches (solution, melting–cooling, and solid state) improved PLQYs up to 86%.

Thrombosis drives myocardial infarction, acute ischaemic stroke and pulmonary embolism, yet more than half of patients undergoing mechanical thrombectomy fail to achieve functional independence, and residual thrombus is detected in up to 91% of cases after catheter-directed thrombolysis. These persistent clinical failures reflect a fundamental gap: thrombus removal is not merely dissolution or extraction, but a multiphysics process governed by the fracture, deformation and fragmentation of a highly anisotropic composite material whose mechanical behavior remains poorly understood and insufficiently predicted. In this short review, we examine the mechanics of thrombi and their data-driven simulation from a failure-oriented perspective. A thrombus is a hierarchically organized composite in which a fibrin network scaffold, functionally differentiated platelet subpopulations and red blood cells interact across scales spanning six orders of magnitude, thus residual thrombus and fragment migration emerge as the fundamental challenges shared by all recanalization strategies from pharmacological thrombolysis to mechanical thrombectomy. We survey six major computational paradigms, including continuum-based approaches, lattice Boltzmann method, particle-based mesoscopic simulation, discrete fiber network models, multiscale coupling frameworks and artificial intelligence/machine learning-assisted methods, and trace the field's evolution over four decades from reaction kinetics to multiphysics fracture coupling. Finally, we delineate critical gaps in current simulation frameworks and argue that AI-driven surrogate models, physics-informed digital twins and generative virtual patient cohorts could transform thrombus simulation from a predominantly academic endeavor into a clinically actionable platform for real-time decision support.

Contactless monitoring of vital signs such as heart rate (HR) and respiratory rate (RR) has gained significant attention, with vibration-based sensors like geophones showing promise for accurate, non-invasive monitoring. However, most existing systems are developed with healthy subjects and may not generalize well to extreme physiological ranges, such as those observed in infants or patients with arrhythmia. Moreover, the underlying mechanisms of cardiorespiratory vibration dynamics remain insufficiently understood, limiting clinical adoption of these systems. To address these challenges, we present a programmable cardiorespiratory testbed capable of generating realistic HR and RR signals across a wide range (HR: 40–240 bpm, RR: 8–40 bpm). Our system uses a voice coil motor that acts as the vibration source, driven by a Raspberry Pi-based control circuit. Unlike similar systems that use separate modules for heart and lung signals, our setup generates both signals using a single motor. The synthetic signals exhibit a strong correlation of 0.85 compared with data from 75 human subjects. We use this system to design signal processing-based algorithms for vital signs monitoring and demonstrate their robustness for extreme physiological ranges. The proposed system enhances the understanding of cardiorespiratory vibration dynamics while significantly reducing the time and effort required to collect real-world data.