Optimizing the Performance of Sodium‐Ion Battery through Suppressing ZnS Anode Alloy Reaction
Bibliographic record
Abstract
Zinc sulfide (ZnS) is a promising anode material for sodium‐ion batteries (SIBs) due to its high theoretical capacity and cost‐effectiveness. However, the alloying reaction of ZnS causes severe volume expansion, leading to material pulverization and capacity decay. To address this, a sandwich‐structured ZnS/porous MXene (ZnS/PMX) composite is designed, where ZnS nanoparticles are anchored on PMX porous layers via ZnOTi interfacial bonding. The nanoporous structure of PMX creates vertical ion transport pathways, enabling faster sodium‐ion diffusion and overcoming the limitations of conventional 2D MXene. Additionally, the confinement effect of PMX suppresses the alloying reaction of ZnS, enhancing its structural stability. As an SIB anode, ZnS/PMX maintains capacities of 414.8 mA h g − 1 after 2100 cycles at 5.0 A g − 1 , 322.9 mA h g − 1 after 3300 cycles at 10.0 A g − 1 , and 276.9 mA h g − 1 after 4100 cycles at 20.0 A g − 1 . This performance benefits from the confinement effects of PMX, which effectively suppresses the alloying reaction and enhances ZnS stability. The results shed new light on the design of metal sulfide/MXene hybrid materials for alkali metal batteries.
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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".