A Tellurium‐Boosted High‐Areal‐Capacity Zinc‐Sulfur Battery
Bibliographic record
Abstract
Abstract Aqueous rechargeable zinc‐sulfur (Zn‐S) batteries are a promising, cost‐effective, and high‐capacity energy storage technology. Still, they are challenged by the poor reversibility of S cathodes, sluggish redox kinetics, low S utilization, and unsatisfactory areal capacity. This work develops a facile strategy to achieve an appealing high‐areal‐capacity (above 5 mAh cm −2 ) Zn‐S battery by molecular‐level regulation between S and high‐electrical‐conductivity tellurium (Te). The incorporation of Te as a dopant allows for manipulation of the Zn‐S electrochemistry, resulting in accelerated redox conversion, and enhanced S utilization. Meanwhile, accompanied by the S‐ZnS conversion, Te is converted to zinc telluride during the discharge process, as revealed by ex‐situ characterizations. This additional redox reaction contributes to the S cathode's total excellent discharge capacity. With this unique cathode structure design, the carbon‐confined TeS cathode (denoted as Te 1 S 7 /C) delivers a high reversible capacity of 1335.0 mAh g −1 at 0.1 A g −1 with a mass loading of 4.22 mg cm −2 , corresponding to a remarkable areal capacity of 5.64 mAh cm −2 . Notably, a hybrid electrolyte design uplifts discharge plateau, reduces overpotential, suppresses Zn dendrites growth, and extends the calendar life of Zn‐Te 1 S 7 batteries. This study provides a rational S cathode structure to realize high‐capacity Zn‐S batteries for practical applications.
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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".