Investigation of Cathode Structure and Electrolyte Chemistry for Emerging Metal-Tellurium Batteries
Bibliographic record
Abstract
Tellurium (Te) has received rising attention as electrode materials in next-generation high-energy-density rechargeable batteries due to its superior electronic conductivity and comparable specific volumetric capacity compared to conversion-type sulfur or selenium. To date, there is a lack of comprehensive understanding regarding the fundamental electrochemistry, structure design and electrolyte chemistry in emerging metal-Te battery systems. Herein, extensive efforts have been made in our group to figure out the role of carbon host in Te/C cathode architecture and construct highly stable Te/C cathodes. Our finding is that an ideal porous carbon is required to possess a majority of micropores to confine Te active materials and a small portion of mesopores to facilitate electrolyte wetting and Li-ion transport. Importantly, a durable Li-Te battery over 1,000 cycles at 2C was achieved with microporous carbon as Te host to constrain volume change of Te. A quasi-solid-state Li-Te is also constructed and demonstrates superior cycling and rate performance than Li-S/Se batteries with the same cell configuration. Moreover, the electrolyte chemistry and reaction mechanism in K-Te battery system are comprehensively revealed from the aspects of redox kinetics and surface chemistry. The two electrolyte salts (potassium hexafluorophosphate, KPF6 and potassium bis(fluorosulfonyl)imide, KFSI) induce similar phase transformation but different specific capacity, reaction kinetics, and SEI composition on the Te/C cathode. These findings are expected to promote the development of Te-based next-generation energy storage systems.
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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 | 0.001 |
| 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".