High-performance and reusable energy-absorbing materials have tremendous potential in industrial applications. Achieving both high performance and reusability has long been a challenge due to their apparent incompatibility. To address this, we proposed a solid-liquid dual-state mechanical metamaterial. This meta-material exhibits robust mechanical properties when the liquid metal is solid and achieves high energy absorption through its plastic deformation. Upon heating-induced solid-liquid state transition, its deformed state fully recovers its initial state, ensuring reusability. The metamaterials can be fabricated by injecting liquid metal into an hollow elastic lattice structure manufactured through additive manufacturing processes. The mechanical properties of solid-liquid dual-state mechanical metamaterials prepared from different liquid metal, such as gallium, Field's metal, and Wood's metal, are analyzed in this paper through a combined approach of experiments, theoretical analysis, and numerical simulations. The results reveal that the proposed metamaterial significantly outperforms all previously reported reusable energy-absorbing materials in specific energy absorption (SEA). This breakthrough driven by the solid-liquid state transition redefines the limits of reusable energy absorption and opens the path to develop a complete family of robust, reusable materials.
Rare genetic factors have been shown to substantially contribute to mental illness, but so far, no precision treatments for mental disorders have been described. It was recently identified that rare variants in GRIN2A encoding the GluN2A subunit of the N-methyl-D-aspartate receptor (NMDAR) confer a substantial risk for schizophrenia. To determine the prevalence of mental disorders among individuals with GRIN2A-related disorders, we enquired the presence of psychiatric symptoms in 235 individuals with pathogenic variants in GRIN2A who had previously enrolled in our global GRIN registry. We identified null variants in GRIN2A (GRIN2Anull) to be significantly associated with a broad spectrum of mental disorders including schizophrenia compared to a longitudinal population cohort (FinRegistry) as well as missense variants (GRIN2Amissense). In our cohort, GRIN2Anull-related mental disorders manifest in early childhood or adolescence, which is substantially earlier than the average adult onset in the general population. In 68% of co-incident epilepsy and mental disorder, mental disorders start after epilepsy offset and the age of epilepsy offset correlated with mental disorder onset. GRIN2Anull-related phenotypes appear to occasionally even manifest as isolated mental disorder, i.e. as schizophrenia or mood disorder without further GRIN2A-specific symptoms, such as intellectual disability and/or epilepsy. As L-serine is known to mediate co-agonistic effects on the NMDAR, we applied it to four individuals with GRIN2Anull-related mental disorders, all of whom experienced improvements of their neuropsychiatric phenotype. GRIN2Anull appears to be the first monogenic cause of early-onset and even isolated mental disorders, such as early-onset schizophrenia. Genetic testing should be considered in the diagnostic work-up of affected individuals to improve diagnosis and potentially offer personalized treatment as increasing brain concentrations of NMDAR co-agonists appears to be a promising precision treatment approach successfully targeting deficient glutamatergic signaling in individuals with mental disorders, i.e. due to GRIN2Anull.
Guided elastic waves are a truly cross-disciplinary key enabling technology. For more than five decades, surface acoustic wave (SAW) and bulk acoustic wave devices find widespread applications. Nowadays, different types of guided elastic waves cover the wide spectrum of applications spanning from quantum technologies to the life sciences, from controlling single excitations to macroscopic collective states in condensed matter. Six years after the first 2019 SAW roadmap, we believe it is time to make a step back and take a fresh look at the status of the field and its future challenges. Since the first roadmap in 2019, the spectrum clearly expanded and this new edition presents a current snapshot of the status of this vibrant field and prospects for potential future developments.
Tubular lattice metamaterials are prized for their lightweight nature and exceptional mechanical properties, particularly resistance to bending and buckling. However, their performance is inherently limited by hollow nodal connections, which act as stress-concentrating geometric imperfections that compromise stiffness, strength, and stability. Inspired by the reinforced skeletal architecture of the seahorse tail, we introduce a novel alternating collinear plate-reinforced tubular (ACPT) lattice metamaterial. Through integrated simulation and experimental analysis, we demonstrate that our bioinspired design eliminates these detrimental hollow nodes. The ACPT lattice achieves remarkable enhancements over conventional simple cubic tubular (SCT) lattices, including a 219% increase in Young’s modulus and a 120% increase in yield strength. The hybrid plate reinforcement simultaneously boosts buckling resistance, resulting in a 59% improvement in specific energy absorption and superior compressive stability. Furthermore, we show that the elastoplastic properties and large deformation behavior can be effectively tuned via the plate-to-tube thickness ratio. These demonstrable advantages underscore the ACPT lattice’s high potential for advanced lightweight applications requiring exceptional load-bearing capacity and energy absorption, showcasing a successful bioinspired strategy to overcome the inherent limitations of conventional lattice metamaterials.
Proton exchange membrane fuel cell (PEMFC) is used in several fields due to its high efficiency and environmental friendliness. Durability issues remain one of the main barriers limiting its large-scale commercialization. Although prognostic techniques can optimize operation and extend fuel cell lifetime, their accuracy is often compromised by recoverable fault behaviors. Therefore, addressing this interference has become a crucial challenge in achieving reliable PEMFC prognostics. To tackle these issues, a diagnostic-prognostic hybrid framework integrating data-driven recoverable fault recognition with model-based degradation prediction is proposed. Specifically, a recoverable fault recognition model is developed to determine the operational state of the fuel cell, where data augmentation is employed to mitigate the adverse effects of data imbalance on classification performance. Using a prognostic strategy-guided particle filter (PSG-PF) model for degradation trend prediction and remaining useful life (RUL) estimation. The proposed method is evaluated by the dynamic load aging experimental data of PEMFC. The results show that the proposed method achieves over 98% accuracy in recoverable fault recognition. Meanwhile, by effectively circumventing the impact of recoverable faults, the average relative accuracy of predicted RUL reached 86%. The proposed framework can achieve reliable recognition of recoverable faults and credible RUL prediction under complex operating conditions. It enhances the robustness of PEMFC prognostics, supporting longer lifetime and more stable operation of fuel cells.
