Spatially Confined “Edge‐to‐Edge” Strategy for Achieving Compact Na<sup>+</sup>/K<sup>+</sup> Storage: Constructing Hetero‐Ni/Ni<sub>3</sub>S<sub>2</sub> in Densified Carbons
Bibliographic record
Abstract
Abstract Transition metal sulfides (TMS) are considered as promising anodes for sodium/potassium ion batteries (SIBs/PIBs), and compositing TMS with conductive nanocarbons is an effective mitigation for improving rate performance and cycling stability. However, such a coupling strategy often decreases the tap density and therefore the volumetric energy of electrode. To achieve fast electron/ion transport and high volumetric capacity simultaneously, herein, a compact nanostructure with hetero Ni‐Ni3S2 nanoparticles embedded in a densified S‐doped carbon matrix (Ni‐Ni3S2@SC) is constructed via a spatially confined “edge‐to‐edge” strategy. Experimental and theoretical results confirm that the carbon matrix and metallic Ni nanoparticles provide fast electron transport pathways at two scales, while the abundant heterojunctions with strong electric fields promote the ion migration and Na/K adsorption. As an anode in SIBs/PIBs, the Ni‐Ni3S2@SC exhibits superior rate capability (289/197 mA h g−1 at 2 A g−1), stable cycling performance (88.1/86.2% capacity retention after 100 cycles), and exceptional volumetric capacity (1048/850 mAh cm−3 at 0.05 A g−1). The impressive energy‐power characteristics for Ni‐Ni3S2@SC anode are further confirmed in full cell batteries and hybrid capacitors. The reported spatially confined “edge‐to‐edge” strategy might be adapted to the construction of various binary and/or ternary metal sulfide dense electrodes for advanced energy storage devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".