Mechanisms for Improved Anode Performance in Titanium Niobate via Neodymium Doping
Bibliographic record
Abstract
High-powered Li-ion battery anodes are a rapidly developing class of materials, being essential to the success of various applications, such as medical devices and heavy machinery. One material of significant recent interest is TiNb 2 O 7, which is attractive due to its high theoretical capacity of 388 mA h g –1 . This capacity is not typically achieved, but our recent efforts to improve the performance of TiNb 2 O 7 by substitutional doping with Nd have increased the capacity by 19% over the undoped material to 321 mA h g –1 with only a 1.2% substitution. In this work, experimental investigations of lithium and electron transport properties revealed that the enhanced performance of Nd-doped TiNb 2 O 7 is primarily due to an increased number of lithium storage sites and improved accessibility through more efficient diffusion pathways. X-ray absorbance spectroscopy and computational modeling were used to understand the mechanism behind these improvements. The two methods suggest that Nd in the TiNb 2 O 7 lattice increases the size of certain channels for lithium diffusion while decreasing the size of others. The fact that this results in faster diffusion and increased lithium storage capacity demonstrates that those paths that increased in size act as bottlenecks in the undoped TiNb 2 O 7 . This complement of experiment and computation guides the further design of these materials by identifying the key structural limitations in the material.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".