Mega High Utilization of Sodium Metal Anodes Enabled by Single Zinc Atom Sites
Bibliographic record
Abstract
Low utilization of active metallic sodium (Na) and uncontrollable growth of Na dendrites remain significant challenges for high-performance Na metal batteries, which are limited to inefficient Na utilization (<1%) and shallow cycling conditions (0.25–1.0 mAh cm –2 ). In this work, a kind of Na metal anode with record-high utilization and long-term cycling stability is reported, using carbon-substrate-supported nitrogen-anchored zinc (Zn) single atoms as a current collector. Single Zn atom sites which serve as a strong “magnet” for Na ions, can guide the metallic Na uniform nucleation and free from dendrite-induced short circuit. The nucleation overpotential of our strategy is essentially zero, where most of the reported modified substrates were greatly exceed 20 mV. Specifically, the Na anodes exhibit a high Na stripping/plating Coulombic efficiency with 99.8% over 350 cycles and a stable voltage response with small voltage hysteresis after cycling 1000 h. The full cell exhibits high Na utilization up to 100% and superior long-term cycling stability for more than 1000 cycles with excellent capacity retention. In terms of lifetime and Na utilization, the Na metal anodes based on our strategy significantly outperforms the reported state-of-the-art Na metal anodes. Moreover, this affords new insights into the controllable Na nucleation behavior and high Na utilization and sheds fresh light on atomic level design of an electrode for Na metal anodes.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".