Mechanistic Insights into the Surface Instabilities of TiNb<sub>2</sub>O<sub>7,</sub> a High‐Power Li‐Ion Anode
Bibliographic record
Abstract
Abstract TiNb2O7 (TNO) is a promising Li‐ion battery anode for high‐power applications, such as implantable medical devices and heavy‐duty equipment. Hailed as being safe due to its elevated operating potential near 1.6 V, TNO has long been assumed to be highly stable in the carbonate‐based electrolytes used in Li‐ion batteries. Herein, all mechanisms occurring at the surface of both TNO and Nd‐doped TNO are identified, and both materials in fact show significant gassing. CO2 is even released at open circuit conditions, demonstrating the poor chemical stability of the material in the electrolyte even prior to battery operation. Such extreme instability is a critical safety concern. In addition, it was found that Ti dissolves from the surface of TNO particles at low voltage (below 1.4 V vs Li), and in fact deposits on the counter electrode. Ti further inside TNO particles then diffuses to the Ti‐poor surface during discharge. Partial carbon‐coating as a mitigating measure has also been tested and found to exacerbate these processes. The findings identify novel reactions occurring within TNO, and clearly highlight the need to stabilize the surfaces of TNO in order to prevent such aggressive deterioration at the surface of the particles.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".