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We show that water-induced decomposition reduces magnon thermal conductivity in a spin-ladder polycrystal, while the absence of grain boundaries or a metal coating prevents degradation, ensuring stability for thermal management applications.

Spin excitations, including magnons and spinons, can carry thermal energy and spin information. Studying spin‐mediated thermal transport is crucial for spin caloritronics, enabling efficient heat dissipation in microelectronics and advanced thermoelectric applications. However, designing quantum materials with controllable spin transport is challenging. Here, highly textured spin‐chain compound Ca₂CuO₃is synthesized using a solvent‐cast cold pressing technique, aligning 2D nanostructures with spin chains perpendicular to the pressing direction. The sample exhibits high thermal conductivity anisotropy and an excellent room‐temperature thermal conductivity of 12 ± 0.7 W m⁻¹K⁻¹, surpassing all polycrystalline quantum magnets. Such a high value is attributed to the significant spin‐mediated thermal conductivity of 10 ± 1 W m⁻¹K⁻¹, the highest reported among all polycrystalline quantum materials. Analysis through a 1D kinetic model suggests that near room‐temperature, spinon thermal transport is dominated by coupling with high‐frequency phonons, while extrinsic spinon‐defect scattering is negligible. Additionally, this method is used to prepare textured La₂CuO₄, exhibiting highly anisotropic magnon thermal transport and demonstrating its broad applicability. A distinct role of defect scattering in spin‐mediated thermal transport is observed in two spin systems. These findings open new avenues for designing quantum materials with controlled spin transport for thermal management and energy conversion.

Biological systems, including proteins, employ water-mediated supramolecular interactions to adopt specific conformations for their functions. However, current solid-state supramolecular materials are typically stiff and fail to capture the dynamic behaviors observed in proteins. Here, we present dynamic crystal-hydrates of aliphatic dipeptides with sequence-isomers of leucine (L) and isoleucine (I). These crystals exhibit shallow conformational energy landscapes, with various reconfigurable crystal nano-architectures accessible through small changes in relative humidity and temperature. Specifically, for LI crystals, as water content changes, the solid-state supramolecular architecture rapidly and reversibly transitions between perpendicular and parallel honeycomb nano-architectures, as well as layered van der Waals structures, leading to significant and distinct variations in mechanical and photophysical properties. Our findings demonstrate the potential of leveraging aliphatic hydrophobic domains inspired by protein architectures to create dynamic solid-state materials with context-adaptive properties.

In this work, we propose a method to construct a uniform error bound for the SK predictor. In investigating the asymptotic properties of the proposed uniform error bound, we examine the convergence rate of SK’s predictive variance under the supremum norm in both fixed and random design settings. Our analyses reveal that the large-sample properties of SK prediction depend on the design-point sampling scheme and the budget allocation scheme adopted. Appropriately controlling the order of noise variances through budget allocation is crucial for achieving a desirable convergence rate of SK’s approximation error, as quantified by the uniform error bound, and for maintaining SK’s numerical stability. Moreover, we investigate the impact of noise variance estimation on the uniform error bound’s performance theoretically and numerically. We demonstrate the superiority of the proposed uniform bound to the Bonferroni correction-based simultaneous confidence interval under various experimental settings through numerical evaluations.

Biological water-responsive materials that deform with changes in relative humidity have recently demonstrated record-high actuation energy densities, showing promise as high-performance actuators for various engineering applications. However, there is a lack of theories capable of explaining or predicting the stress generated during water-responsiveness. Here, we show that the nanoscale confinement of water dominates the macroscopic dehydration-induced stress of the regenerated silk fibroin. We modified silk fibroin’s secondary structure, which leads to various distributions of bulk-like mobile and tightly bound water populations. Interestingly, despite these structure variations, all silk samples start to exert force when the bound-to-mobile (B/M) ratio of confined water reaches the same level. This critical B/M water ratio suggests a common threshold above which the chemical potential of water instigates the actuation. Our findings serve as guidelines for predicting and engineering silk’s WR behavior and suggest the potential of describing the WR behavior of biopolymers through confined water.

This paper proposes two sequential metamodel‐based methods for level‐set estimation (LSE) that leverage the uniform bound built on stochastic kriging: predictive variance reduction (PVR) and expected classification improvement (ECI). We show that PVR and ECI possess desirable theoretical performance guarantees and provide closed‐form expressions for their respective sequential sampling criteria to seek the next design point for performing simulation runs, allowing computationally efficient one‐iteration look‐ahead updates. To enhance understanding, we reveal the connection between PVR and ECI's sequential sampling criteria. Additionally, we propose integrating a budget allocation feature with PVR and ECI, which improves computational efficiency and potentially enhances robustness to the impacts of heteroscedasticity. Numerical studies demonstrate the superior performance of the proposed methods compared to state‐of‐the‐art benchmarking approaches when given a fixed simulation budget, highlighting their effectiveness in addressing LSE problems.