High-entropy strategy to suppress volumetric strain and enhance diffusion rate of Na3V2(PO4)2F3 cathode for durable and high-areal-capacity zinc-ion battery pouch cells
Bibliographic record
Abstract
Aqueous zinc-ion batteries are pivotal contributors to the global energy transformation. Cathode materials with NASICON-type structures are promising candidates for zinc-ion batteries but are hindered by their low electrical conductivity , sluggish ionic diffusion, and structural instability. This work introduces a high-entropy, carbon-coated NASICON-type Na 3 V 2 (PO 4 ) 2 F 3 (HE-NVPF@C) cathode by incorporating five metal elements (Al, Zn, Mn, Cr, and Nb) mainly into the V sites of the VO 4 F 2 octahedral structure. Systematic experimental and simulation studies of the Zn 2+ storage mechanism in high-entropy NASICON-type cathode are presented for the first time. The high-entropy doping strategy contributes to significantly enhanced cycling stability by suppressing Jahn-Teller distortion, reducing lattice change during Zn 2+ extraction and insertion, and decreasing the Zn 2+ migration energy barrier. As a result, the HE-NVPF@C cathode demonstrates exceptional cycling stability over 6000 cycles at 20 C with a capacity loss of a mere 0.0031 % per cycle and a high areal capacity retention of 2.17 mAh cm −2 . In addition, the pouch cell provides a long cycling lifespan with 90.8 % capacity retention at 5 C after 200 cycles. This feasible high-entropy approach broadens the perspective for developing practical zinc-ion batteries with a long cycle lifespan and high areal capacity.
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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".