A Stable Lithium-ion Selenium Batteries Enabled by Microporous Carbon/Se and Fluoroethylene Carbonate Additive
Bibliographic record
Abstract
Lithium-ion batteries are among the most promising rechargeable batteries, due to their exceptional power and energy characteristics. However, they cannot meet the increasing demand from end-users for higher energy density. Herein, we reported a lithium-ion selenium battery by using lithiated graphite/silicon anode and microporous carbon/Se (MPC/Se) cathode with a fluoroethylene carbonate (FEC)-containing carbonate electrolyte. FEC additive enabled a stable MPC/Se cathode due to the suppressed dissolution of polyselenides into the electrolyte and improved the mechanical stability-integrity of graphite/silicon anode, leading to the formation of stable solid electrolyte interphase (SEI) layer on both the cathode and anode surfaces. As a result, lithium-ion selenium battery delivered a high specific capacity and excellent stability. The results showed a specific capacity of 356 mAh g-1 (normalized to Se) and Coulombic efficiency of 99.9% after 125 cycles for the full cells.
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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".