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Artificial engineering often relies on scarce materials, possessing ecological, social, and political risks. In contrast, nature achieves advanced functionalities using only abundant elements and water. Despite this simple insight, incorporating nature-inspired devices made from readily available resources into industrial applications remains a formidable challenge. Here, we present a sustainable 'blue battery'-a water-based energy storage device that mimics natural processes and reduces dependence on scarce materials. This battery harnesses the distinctive electrical properties of water confined within nanometer-scale pores, enabling construction from abundant, non-toxic materials using scalable nanofabrication techniques. It operates without unwanted side reactions, maintains nearly 100% efficiency after 60,000 charge-discharge cycles, and supports a voltage window of up to 1.65 V while achieving competitive power and energy density. The core component, a van der Waals clay nanostructure, uses water as the sole electrolyte with enhanced activity due to interactions with its 2D surface. This water-driven technology offers a sustainable platform for energy systems, with potential applications from small-scale electronics to global energy infrastructure-even in extreme environments like Mars. Our work advances nature-inspired energy solutions, opening new possibilities for the sustainable engineering of blue devices., Comment: Main text: 17 pages, 4 figures, 50 references Suplementary: 29 pages, 26 figures, 3 tables, 3 references

Solar redox flow batteries (SRFB) have received much attention as an alternative integrated technology for simultaneous conversion and storage of solar energy. Yet, the photocatalytic efficiency of semiconductor-based single photoelectrode, such as hematite, remains low due to the trade-off between fast electron hole recombination and insufficient light utilization, as well as inferior reaction kinetics at the solid/liquid interface. Herein, we present an {\alpha}-Fe2O3/CuxO p-n junction, coupled with a readily scalable nanostructure, that increases the electrochemically active sites and improves charge separation. Thanks to light-assisted scanning electrochemical microscopy (Photo-SECM), we elucidate the morphology-dependent carrier transfer process involved in the photo-oxidation reaction at a {\alpha}-Fe2O3 photoanode. The optimized nanostructured is then exploited in the {\alpha}-Fe2O3/CuxO p-n junction, achieving an outstanding unbiased photocurrent density of 0.46 mA/cm2, solar-to-chemical (STC) efficiency over 0.35% and a stable photocharge-discharge cycling. The average solar-to-output energy efficiency (SOEE) for this unassisted {\alpha}-Fe2O3-based SRFB system reaches 0.18%, comparable to previously reported DSSC-assisted hematite SRFBs. The use of earth-abundant materials and the compatibility with scalable nanostructuring and heterojunction preparation techniques, offer promising opportunities for cost-effective device deployment in real-world applications.

In this paper, we propose a randomly projected convex clustering model for clustering a collection of $n$ high dimensional data points in $\mathbb{R}^d$ with $K$ hidden clusters. Compared to the convex clustering model for clustering original data with dimension $d$, we prove that, under some mild conditions, the perfect recovery of the cluster membership assignments of the convex clustering model, if exists, can be preserved by the randomly projected convex clustering model with embedding dimension $m = O(\epsilon^{-2}\log(n))$, where $0 < \epsilon < 1$ is some given parameter. We further prove that the embedding dimension can be improved to be $O(\epsilon^{-2}\log(K))$, which is independent of the number of data points. Extensive numerical experiment results will be presented in this paper to demonstrate the robustness and superior performance of the randomly projected convex clustering model. The numerical results presented in this paper also demonstrate that the randomly projected convex clustering model can outperform the randomly projected K-means model in practice.