Jiance Sun | Searcharxiv

arXiv2026

Electride States and Superconductivity in Dense Potassium Carbides

Metal carbides have attracted a great deal of attention due to their diverse geometric motifs, remarkable physicochemical properties, and widespread practical applications. However, there is still a lack of systematic understanding regarding the phase diagram of pressurized potassium carbide. Employing first-principles swarm-intelligence structure prediction approach, we comprehensively explore the binary potassium-carbon phases under compression and identify a series of new K-rich and C-rich stoichiometric compounds. Among them, K7C with high K concentration manifests a monoclinic structure with space group C2/m and is predicted to be an electride with zero-dimensional (0D) interstitial electrons. C-abundant KC has an orthorhombic configuration with symmetry Imma, with the carbon atoms arranged in a zigzag pattern. Strikingly, KC3, belonging to a monoclinic C2/m structure, possesses the highest carbon content and features a crumpled honeycomb carbon layer. Furthermore, calculations of electron-phonon coupling reveals that K7C is a 0D electride superconductor with a transition temperature (Tc) of 0.6 K at a pressure of 25 GPa. By contrast, KC exhibits a maximum Tc of 21.4 K at 25 GPa, which is primarily attributed to the robust coupling between low-frequency K- and C-derived phonon modes and C 2p electrons at the Fermi level. In addition, KC3 is calculated to have a Tc value of 6.7 K at 25 GPa. This study provides valuable insights into K-C compounds and broadens the diversity of metal carbide superconductors.

cond-mat.supr-con↗

arXiv2025

SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation

Remote sensing image segmentation is crucial for environmental monitoring, disaster assessment, and resource management, but its performance largely depends on the quality of the dataset. Although several high-quality datasets are broadly accessible, data scarcity remains for specialized tasks like marine oil spill segmentation. Such tasks still rely on manual annotation, which is both time-consuming and influenced by subjective human factors. The segment anything model 2 (SAM2) has strong potential as an automatic annotation framework but struggles to perform effectively on heterogeneous, low-contrast remote sensing imagery. To address these challenges, we introduce a novel label enhancement and automatic annotation framework, termed SAM2-ELNet (Enhancement and Labeling Network). Specifically, we employ the frozen Hiera backbone from the pretrained SAM2 as the encoder, while fine-tuning the adapter and decoder for different remote sensing tasks. In addition, the proposed framework includes a label quality evaluator for filtering, ensuring the reliability of the generated labels. We design a series of experiments targeting resource-limited remote sensing tasks and evaluate our method on two datasets: the Deep-SAR Oil Spill (SOS) dataset with Synthetic Aperture Radar (SAR) imagery, and the CHN6-CUG Road dataset with Very High Resolution (VHR) optical imagery. The proposed framework can enhance coarse annotations and generate reliable training data under resource-limited conditions. Fine-tuned on only 30% of the training data, it generates automatically labeled data. A model trained solely on these achieves slightly lower performance than using the full original annotations, while greatly reducing labeling costs and offering a practical solution for large-scale remote sensing interpretation.

cs.CV↗