Metallic lithium anodes possess the lowest redox potential (−3.04 V vs. SHE) and an ultra-high theoretical capacity (3860 mAh g⁻¹, 2061 mAh cm⁻³). However, during electrochemical cycling, lithium metal tends to plate unevenly, leading to the formation of lithium dendrites. Moreover, severe electrochemical corrosion occurs at the interface between metallic lithium and traditional copper foil current collectors. To address these issues, we selected corrosion-resistant carbon paper as a lithium metal host and modified a uniform distribution of silver nanoparticles and a F-doped amorphous carbon structure as a highly lithiophilic F-CP@Ag host to enhance lithium-ion transport kinetics and achieve improved affinity with lithium metal. The silver nanoparticles reduced the lithium nucleation energy barrier, while F doping resulted in a LiF-rich solid electrolyte interphase that better accommodated volume changes in lithium metal. These two strategies worked together to ensure uniform and stable lithium metal plating/stripping on the F-CP@Ag host. Consequently, under the conditions of 1 mA cm⁻² and 1 mAh cm⁻², the symmetric cell exhibited stable cycling with a polarization voltage of 8 mV for up to 1400 h. This work highlights the corrosion problem of lithium metal on traditional copper foil current collectors and provides guidance for the long-term cycling stability of lithium metal anodes.

Magnetic Resonance Imaging (MRI) Super-Resolution (SR) addresses the challenges such as long scan times and expensive equipment by enhancing image resolution from low-quality inputs acquired in shorter scan times in clinical settings. However, current SR techniques still have problems such as limited ability to capture both local and global static patterns effectively and efficiently. To address these limitations, we propose Delta-WKV, a novel MRI super-resolution model that combines Meta-in-Context Learning (MiCL) with the Delta rule to better recognize both local and global patterns in MRI images. This approach allows Delta-WKV to adjust weights dynamically during inference, improving pattern recognition with fewer parameters and less computational effort, without using state-space modeling. Additionally, inspired by Receptance Weighted Key Value (RWKV), Delta-WKV uses a quad-directional scanning mechanism with time-mixing and channel-mixing structures to capture long-range dependencies while maintaining high-frequency details. Tests on the IXI and fastMRI datasets show that Delta-WKV outperforms existing methods, improving PSNR by 0.06 dB and SSIM by 0.001, while reducing training and inference times by over 15\%. These results demonstrate its efficiency and potential for clinical use with large datasets and high-resolution imaging.