Titanium Dioxide/Lithium Phosphate Nanocomposite Derived from Atomic Layer Deposition as a High‐Performance Anode for Lithium Ion Batteries
Bibliographic record
Abstract
Atomic layer deposition (ALD) is considered as a powerful technique to synthesize novel electrode materials for lithium‐ion batteries (LIBs), because not only the compositions can be specifically designed to achieve higher battery performances, but also the materials can be deposited on various substrates for different purposes. Herein, a novel design of active material/electrolyte mixture electrode, i.e., titanium dioxide/lithium phosphate (TLPO) nanocomposite, has been successfully developed by ALD and deposited on carbon nanotube substrates (CNTs@TLPO) at 250 °C, by combining the ALD recipes of TiO 2 and lithium phosphate (LPO). In the nanocomposite, TiO 2 forms anatase nanocrystals, embedded in a matrix of amorphous lithium phosphate. CNTs@TLPO has been examined as an anode material for LIBs, exhibiting a similar electrochemical response as anatase TiO 2 in the cyclic voltammetry testing. CNTs@TLPO presents an outstanding capacity of 204 mA h g −1 upon 200 cycles in charge and discharge cycling measurements, as well as a significantly improved rate capability compared with ALD deposited TiO 2 on CNTs without LPO ALD cycles. This work shows that the in situ addition of solid‐state electrolyte (e.g., lithium phosphate), which introduces higher Li + ionic conductivity, is an efficient way to achieve high‐performance electrode materials for LIBs by ALD.
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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".