The bending fatigue resistance of superelastic shape memory alloys (SMAs) is a key determinant for their reliable function in cyclic applications such as biomedical implants, adaptive actuators, and elastocaloric devices. However, conventional NiTi alloys exhibit limited fatigue life due to premature crack initiation and propagation under cyclic tensile loading. Here, we report a surface engineering strategy that overcomes this limitation by inducing a hierarchical surface architecture via pre-strain warm laser shock peening (pw-LSP). This architecture integrates a high-strength titanium nitride-enriched top layer, an ultrafine-grained layer with an inverse grain size gradient and a B19′–R–B2 phase gradient, and a substantial compressive residual stress exceeding 1 GPa. These features act synergistically to suppress crack nucleation and arrest propagation through a crack-tip shielding mechanism. As a result, the treated NiTi demonstrates a bending fatigue life exceeding 5 million cycles at a maximum surface tensile strain of 1.94%—representing a more than 3000-fold enhancement over untreated nanocrystalline NiTi. This work presents a robust and scalable approach for designing fatigue-resistant SMAs with broad implications for high-cycle, high-reliability applications. Laser-based surface treatment creates a hierarchical architecture in superelastic NiTi, integrating hard nitrides, graded grains and phases, and high compressive stress. This synergy suppresses cracking and extends bending fatigue life beyond 5 million cycles.

This folder contains the raw data that was used for quantifications (and the quantifications in an Excel sheet). The raw data (.txt files called EasyQuant files) was extracted from autoradiographs of SDS-PAGE gels using the ImageGauge software from Fuji (associated with the Fuji gel scanner). This machine has since been discontinued (these data were collected 2014-2016). The .txt files were imported into EasyQuant and fitted to a Gaussian distribution automatically by the software (software developed in the Gunnar von Heijne lab by Dr. Rickard Hedman), and the Ffl (fraction full length) was calculated.

In this work, we assess the accuracy of the approximate fourth-order N-electron valence perturbation theory (NEVPT4(SD)) methodology for computing excited states of organic molecules. The well-established Thiel benchmark set was employed, comprising 225 vertical excitations spanning π → π*, <i>n</i> → π*, and σ → π* transition types. A state-specific canonicalization procedure was applied, enabling a direct comparison with CC3 reference data reported by Schreiber et al. <i>J. Chem. Phys.</i>, <b>2008</b>, <i>128</i>, 134110. For both singlet and triplet excitations, NEVPT4(SD) systematically outperforms lower-order NEVPT variants, as well as previously reported complete active space second-order perturbation theory (CASPT2) results. A detailed analysis of the singlet excitations reveals that <i>n</i> → π* transitions have a slight tendency to be overestimated (by about 0.1 eV), while π → π* excitations tend to be slightly underestimated (by -0.04 eV). While this shift persists across all NEVPT perturbation orders, its magnitude decreases with higher-order treatments. Across the entire test set, NEVPT4(SD) has a very narrow error distribution with a peak very close to 0. Thus, this study demonstrates the robustness and high accuracy of NEVPT4(SD) for vertical excitation energies, highlighting its clear advantages over lower-order perturbative approaches while remaining computationally much more affordable than other multireference correlation approaches that proceed beyond second-order perturbation theory.

Understanding how grain boundaries mediate fracture remains a critical challenge in designing ductile, high-performance refractory alloys. Here, we extend the Rice-Thomson criterion to account for the angle between cracks and the impinging grain boundaries (GBs), capturing the competition between intergranular fracture and dislocation-mediated plasticity. Using machine learning interatomic potentials, we performed molecular statics simulations to probe fracture mechanisms in nanocrystalline NbMoTaW and Nb₄₅Ta₂₅Ti₁₅Hf₁₅, each with two different grain sizes, revealing trends consistent with experimental observations and the extended Rice model. Comparison with averaged R-curves for bulk samples demonstrates that GBs enhance ductility in Nb₄₅Ta₂₅Ti₁₅Hf₁₅ in both grain sizes investigated. In contrast, GBs only locally improve fracture resistance in NbMoTaW when cracks are temporarily pinned at GBs inclined at high angles from the crack, but generally promote brittle intergranular fracture. These contrasting behaviors are attributed to differences in GB cohesion, reflecting clear alloying trends that align with ab-initio calculations and trends observed experimentally. Our results bridge classical fracture theory, atomistic simulations, and experimental observations, providing a comprehensive understanding of the fracture mechanisms in nanocrystalline refractory complex concentrated alloys.

Metal–organic frameworks (MOFs), composed of metal nodes coordinated with organic ligands, have emerged as a versatile class of functional materials for next‐generation smart textile systems. Their high surface area, tunable pore chemistry, and modular structural diversity enable multifunctional textile platforms. When integrated into textile substrates, MOFs can retain the properties, breathability, and comfort of fabrics while imparting advanced functionalities significant for sensing, environmental protection, biomedical interfaces, and energy‐related applications. This review provides a comprehensive overview of recent advances in MOF‐integrated smart textiles, focusing on material design principles, integration strategies, and application‐driven performance. Key fabrication approaches including surface coating, in situ growth, post‐synthetic modification, hydrothermal assembly, and emerging printing techniques such as inkjet and electrohydrodynamic jet are critically examined with respect to scalability, durability, and textile compatibility. Representative applications spanning gas and chemical sensing, detoxification, antimicrobial and biomedical functions, energy harvesting, and flexible energy storage are systematically discussed. Finally, current challenges and future opportunities are outlined, highlighting pathways toward scalable, durable, and application‐oriented MOF‐based smart textiles for real‐world applications.

Benthic prokaryotic communities in deep-sea sediments remain poorly studied. They are constrained by organic matter availability and oxygenation in warm deep-sea ecosystems. Here, we investigated benthic prokaryotic communities and carbon uptake in deep Red Sea sediments (218–2415 m seafloor depth), where persistently warm (~21.5 °C) waters and a strong south–north productivity gradient co-occur. Sediment particulate organic carbon (POC), prokaryotic abundance (PA), and [13C]-D-glucose-based carbon uptake and uptake kinetics were examined in two sediment layers (0–1 and 4–5 cm), while bacterial communities were characterized using 16S rRNA gene sequencing of the 0–1 cm layer. Sediment POC, PA, and carbon uptake declined northward, consistent with reduced organic-carbon supply to the seafloor. Bacterial community composition differed significantly across the ~500 m depth associated with the Red Sea oxygen minimum zone (OMZ). Sediments from the relatively low-oxygen upper OMZ-range (200–500 m) had higher sediment POC and PA, and were enriched in putatively anaerobe-associated taxa, whereas deeper sediments (&gt;500 m) below the OMZ exhibited more fragmented co-occurrence networks. These results suggest that organic-carbon availability defines the basin-scale metabolic backdrop, whereas bacterial community differentiation was more clearly resolved between upper OMZ-range and below-OMZ sediments than along latitude alone.

The vertebrate brain must balance internally generated predictions with constraints of environmental affordances. This balance constitutes a fundamental principle of neural organization that underwrites cortical computation. Using the prosomeric model of the neuraxis, we show how dorsalizing and ventralizing morphogenetic gradients specify excitatory and inhibitory lineages during development, establishing the functional architecture of active affordance. These developmental asymmetries are elaborated through telencephalic expansion, pallial-subpallial integration, and laminar differentiation of the neocortex, as described by the structural model. We demonstrate that motor control emerges within a sensory-predictive architecture due to the alar origin of the telencephalon and that increasing excitatory-inhibitory complementarity within the mammalian neocortex enables selective, context-sensitive action. Subpallial and diencephalic systems provide inhibitory governance over cortical action tendencies, supporting policy evaluation and selection in the framework of active inference. At the base of this hierarchy, the hypothalamus integrates homeostatic and allostatic signals to bias the landscape of affordances, shaping the likelihood of action policies. Together, these findings establish active affordance as a developmental and evolutionary framework linking prosomeric neurodevelopment, cortical architecture, subcortical control, and adaptive behavior. Active inference is thereby situated as the mature cortical expression of a conserved biological solution to acting in an uncertain world.