High‐Performance Potassium‐Tellurium Batteries Stabilized by Interface Engineering
Bibliographic record
Abstract
Abstract The emerging potassium‐tellurium (K‐Te) battery system is expected to realize fast reaction kinetics and excellent rate performance due to the exceptional electrical conductivity of Te. However, there has been a lack of fundamental knowledge about this new K‐Te system, including the reaction mechanism and cathode structure design. Herein, a two‐step reaction pathway from Te to K2Te3 and ultimately to K5Te3 is investigated in carbonate electrolyte‐based K‐Te batteries by X‐ray diffraction, high‐resolution transmission electron microscopy, and selected area electron diffraction characterizations. Additionally, the atomic layer deposition technique is adopted to deposit an ultrathin aluminum oxide (Al2O3) film on the electrode surface, which induces the generation of a stable solid electrolyte interphase layer and reduces the loss of active materials effectively. Consequently, the rationally fabricated Te/porous carbon cathode with functional Al2O3 coating delivers remarkable long‐term cycling stability over 500 cycles at 1 C with an ultralow capacity decay of only 0.01% per cycle. This interface engineering strategy is validated to stabilize the electrode surface, enhance the structural integrity and ensure reliable electron transfer and K‐ion conduction over repeated potassiation/depotassiation cycles. These findings are expected to promote the development of high‐energy‐density K‐S/Se/Te 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".