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The application of Si-based anodes is mainly impeded by their intrinsic volumetric stress. Recently proposed elastic binders have been demonstrated as an efficient approach to alleviate this large stress, but the excessive dosage of binders leads to inadequate energy density. Herein, a 3D resilient ion/electron dual conductive network (LPCC) composed of carboxylic carbon nanotubes (c-CNTs) interwoven with carboxymethyl chitosan (c-CS) and lithiated poly(acrylic acid) (LiPAA) was developed. The formed covalent and hydrogen bonds can endure and dissipate volumetric stress, thereby maintaining electrode integrity. Additionally, the LPCC network intimately wraps Si–C particles and provides a continuous ion/electron dual conductive skeleton. Therefore, this novel 3D network enables Si–C anodes to deliver superior electrochemical performance even at a high Si–C content of 96 wt%. Moreover, an NCM811‖Si–C/LPCC full-cell (>3.1 mA h cm−2) achieves unprecedented high gravimetric and volumetric energy densities of 510 W h kg−1 and 1173 W h L−1, respectively. This study provides novel insights for developing efficient Si-based binders for next-generation high-energy-density systems. [ABSTRACT FROM AUTHOR]

Unstable solid electrolyte interface (SEI) films, disordered growth of sodium dendrites and relatively large volume expansion of metal sodium are huge bottlenecks for the safety and stability of sodium metal batteries (SMBs). Herein, 2-propyn-1-ol methanesulfonate (PMS) has been proposed as a preferential anode film-forming additive to stabilize Na‖Na3V2(PO4)3 (NVP) batteries. Molecular dynamics simulations indicate that PMS additive can actively induce the solvation behavior of fluoroethylene carbonate (FEC), effectively suppressing further decomposition of solvent molecules. Moreover, in situ optical experiments, scanning electron microscopy, and X-ray photoelectron spectroscopy indicate that PMS and FEC synergistically construct a SEI film rich in inorganic components such as NaF and Na2S, significantly inhibiting the growth of sodium dendrites. Consequently, Na‖NVP full cells in 3 vol% PMS electrolyte exhibit ultra-high average coulombic efficiency of 99.9% and a capacity retention rate of 92% after 1000 cycles at 2C. This work offers an optimistic prospect for achieving safe and stable SMBs. [ABSTRACT FROM AUTHOR]

Due to the inadequacy in utilizing complementary information from different modalities and the biased estimation of degraded parameters, the unsupervised hyperspectral super-resolution algorithm suffers from low precision and limited applicability. To address this issue, this paper proposes an approach for hyperspectral image super-resolution, namely, the Unsupervised Multimodal Multilevel Feature Fusion network (UMMFF). The proposed approach employs a gated cross-retention module to learn shared patterns among different modalities. This module effectively eliminates the intermodal differences while preserving spatial–spectral correlations, thereby facilitating information interaction. A multilevel spatial–channel attention and parallel fusion decoder are constructed to extract features at three levels (low, medium, and high), enriching the information of the multimodal images. Additionally, an independent prior-based implicit neural representation blind estimation network is designed to accurately estimate the degraded parameters. The utilization of UMMFF on the "Washington DC", Salinas, and Botswana datasets exhibited a superior performance compared to existing state-of-the-art methods in terms of primary performance metrics such as PSNR and ERGAS, and the PSNR values improved by 18.03%, 8.55%, and 5.70%, respectively, while the ERGAS values decreased by 50.00%, 75.39%, and 53.27%, respectively. The experimental results indicate that UMMFF demonstrates excellent algorithm adaptability, resulting in high-precision reconstruction outcomes. [ABSTRACT FROM AUTHOR]