Improving Li-Ion Anodes with Systematic Elemental Doping in Titanium Niobate
Bibliographic record
Abstract
Lithium-ion batteries for high-power applications have become an increasingly important area of development as these devices have been used in implantable medical devices, where extreme safety and long lifetimes are essential. TiNb 2 O 7 has emerged as a promising candidate to replace the current industrial standard Li 4 Ti 5 O 12 as a safe high-power anode. In this study, we use combinatorial methods to screen the effects of 52 different dopants (M) in the composition (TiNb 2 ) 0.98 M 0.06 O 7 with 52 unique elemental dopants. The materials were studied with high throughput by X-ray diffraction and cyclic voltammetry to reveal the performance of the doped materials. Structural analysis revealed a change in the lattice parameters dependent on the substituent present, and some extremely large dopants were able to partially substitute into the materials. Several doped materials, particularly with large dopants, show excellent discharge capacities of 326.7 mAh g –1 at room temperature, an improvement of over 20% over the undoped material despite moderate doping levels (2% of the metals). Many of the doped TNO samples show excellent extended cycling, especially at 37 °C. The dramatic improvements with the addition of large dopants (most of which are electrochemically inactive) are attributed to distortions in the local structure improving the Li diffusion paths, thereby enabling higher capacities, and establish a new design principle in optimizing these safe anodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".