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Transforming atmospheric water vapor into liquid form can be a way to supply water to arid regions for uses such as drinking water, thermal management, and hydrogen generation. Many current methods rely on solid sorbents that cycle between capture and release at slow rates. We envision a radically different approach where water is transformed and directly captured into a liquid salt solution that is suitable for subsequent distillation or other processing using existing methods. In contrast to other methods utilizing hydrogels as sorbents, we do not store water within hydrogels—we use them as a transport medium. Inspired by nature, we capture atmospheric water through a hydrogel membrane “skin” at an extraordinarily high rate of 5.50 kg m^-2 d^-1 at a low humidity of 35%. and up to 16.9 kg m^-2 d^-1at higher humidities. For a drinking-water application, calculated performance of a hypothetical one-square-meter device shows that water could be supplied to two to three people in arid environments. This work is a significant step toward providing new resources and possibilities to water-scarce regions. 

$\text{DMRadio-}{\mathrm{m}}^3$is an experimental search for dark matter axions. It uses a solenoidal dc magnetic field to convert an axion dark-matter signal to an ac electromagnetic response in a coaxial copper pickup. The current induced by this axion signal is measured by dc SQUIDs. $\text{DMRadio-}{\mathrm{m}}^3$is designed to be sensitive to Kim-Shifman-Vainshtein-Zakharov (KSVZ) and Dine-Fischler-Srednicki-Zhitnisky (DFSZ) QCD axion models in the 10–200 MHz ( $41\mathrm{neV}/c^2–0.83\mathrm{\mu }\mathrm{eV}/c^2$) range, and to axions with $g_{a\gamma \gamma }=g_{a\gamma \gamma ,\mathrm{DFSZ}}\left(30\mathrm{MHz}\right)=1.87\times {10}^{-17}{\mathrm{GeV}}^{-1}$over 5–30 MHz as an extended goal. In this work, we present the electromagnetic modeling of the response of the experiment to an axion signal over the full frequency range of $\text{DMRadio-}{\mathrm{m}}^3$, which extends from the low-frequency, lumped-element limit to a regime where the axion Compton wavelength is only a factor of 2 larger than the detector size. With these results, we determine the live time and sensitivity of the experiment. The primary science goal of sensitivity to DFSZ axions across 30–200 MHz can be achieved with a $3\sigma$live scan time of 2.9 years. 

We find that the collapse of a droplet on a hydrogel is dictated by competing timescales of contact line advancement and water diffusion into the gel. 

Abstract The galaxy cluster A746 (z= 0.214), featuring a double radio relic system, two isolated radio relics, a possible radio halo, disturbed V-shaped X-ray emission, and intricate galaxy distributions, is a unique and complex merging system. We present a weak-lensing analysis of A746 based on wide-field imaging data from Subaru/Hyper Suprime-Cam observations. The mass distribution is characterized by a main peak, which coincides with the center of the X-ray emission. At this main peak, we detect two extensions toward the north and west tracing the cluster galaxy and X-ray distributions. Despite the ongoing merger, our estimate of the A746 global massM₅₀₀= 4.4 ± 1.0 × 10¹⁴M_⊙is consistent with the previous results from Sunyaev-Zel'dovich and X-ray observations. We conclude that reconciling the distributions of mass, galaxies, and intracluster medium with the double radio relic system and other radio features remains challenging. 

From pasta to biological tissues to contact lenses, gel and gel-like materials inherently soften as they swell with water. In dry, low-relative-humidity environments, these materials stiffen as they de-swell with water. Here, we use semi-dilute polymer theory to develop a simple power-law relationship between hydrogel elastic modulus and swelling. From this relationship, we predict hydrogel stiffness or swelling at arbitrary relative humidities. Our close predictions of properties of hydrogels across three different polymer mesh families at varying crosslinking densities and relative humidities demonstrate the validity and generality of our understanding. This predictive capability enables more rapid material discovery and selection for hydrogel applications in varying humidity environments. 

Bootstrapping has been a primary tool for ensemble and uncertainty quantification in machine learning and statistics. However, due to its nature of multiple training and resampling, bootstrapping deep neural networks is computationally burdensome; hence it has difficulties in practical application to the uncertainty estimation and related tasks. To overcome this computational bottleneck, we propose a novel approach called Neural Bootstrapper (NeuBoots), which learns to generate bootstrapped neural networks through single model training. NeuBoots injects the bootstrap weights into the high-level feature layers of the backbone network and outputs the bootstrapped predictions of the target, without additional parameters and the repetitive computations from scratch. We apply NeuBoots to various machine learning tasks related to uncertainty quantification, including prediction calibrations in image classification and semantic segmentation, active learning, and detection of out-of-distribution samples. Our empirical results show that NeuBoots outperforms other bagging based methods under a much lower computational cost without losing the validity of bootstrapping. 

Hydrogels hold promise in agriculture as reservoirs of water in dry soil, potentially alleviating the burden of irrigation. However, confinement in soil can markedly reduce the ability of hydrogels to absorb water and swell, limiting their widespread adoption. Unfortunately, the underlying reason remains unknown. By directly visualizing the swelling of hydrogels confined in three-dimensional granular media, we demonstrate that the extent of hydrogel swelling is determined by the competition between the force exerted by the hydrogel due to osmotic swelling and the confining force transmitted by the surrounding grains. Furthermore, the medium can itself be restructured by hydrogel swelling, as set by the balance between the osmotic swelling force, the confining force, and intergrain friction. Together, our results provide quantitative principles to predict how hydrogels behave in confinement, potentially improving their use in agriculture as well as informing other applications such as oil recovery, construction, mechanobiology, and filtration.