Microstructure evolution and self‐discharge degradation mechanism in Li/MnO <sub>2</sub> primary batteries
Bibliographic record
Abstract
Li/MnO 2 primary batteries are widely used in industry for their high specific capacity and safety. However, a deep comprehension of the Li + insertion mechanism and the high self‐discharge rate of the batteries is still needed. Here, the storage mechanism of Li + in the tunnel structure of MnO 2 as well as the dissolution and migration of Mn‐ions were investigated based on multi‐scale approaches. The Li/Mn ratio (at%) is determined at about 0.82 when the discharge voltage decreases to 2 V. The limited Li‐ions transport rate in the bulk MnO 2 restrains the reduction reaction, resulting in a low practical specific capacity. Moreover, utilizing spherical aberration‐corrected transmission electron microscopy (TEM) coupled with electron energy loss spectroscopy (EELS), the presence of a mixed valence state layer of Mn 2+ /Mn 3+ /Mn 4+ on the surface of the original 20 nm MnO 2 particles was identified, which could contribute to the initial dissolution of Mn‐ions. The battery separator exhibited channels for Mn‐ions migration and diffusion and aggregated Mn particles. We put forward the discharge and degradation route in the ways of Mn‐ions trajectories, and our findings provide a deep understanding of the high self‐discharge rates and the capacity decay of Li‐Mn primary 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) | 0.000 | 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".