High Substitution Rate in TiO<sub>2</sub> Anatase Nanoparticles with Cationic Vacancies for Fast Lithium Storage
Bibliographic record
Abstract
Doping is generally used to tune and enhance the properties of metal oxides. However, their chemical composition cannot be readily modified beyond low dopant amounts without disrupting the crystalline atomic structure. In the case of anatase TiO 2, we introduce a new solution-based chemical route allowing the composition to be significantly modified, substituting the divalent O 2– anions by monovalent F – and OH – anions resulting in the formation of cationic Ti 4+ vacancies (□) whose concentration can be controlled by the reaction temperature. The resulting polyanionic anatase has the general composition Ti 1– x – y □ x + y O 2–4( x + y ) F 4 x (OH) 4 y, reaching vacancy concentrations of up to 22%, i.e., Ti 0.78 □ 0.22 O 1.12 F 0.4 (OH) 0.48 . Solid-state 19 F NMR spectroscopy reveals that fluoride ions can accommodate up to three different environments, depending on Ti and vacancies (i.e. Ti 3 -F, Ti 2 □ 1 -F, and Ti 1 □ 2 -F), with a preferential location close to vacancies. DFT calculations further confirm the fluoride/vacancy ordering. When its characteristics were evaluated as an electrode for reversible Li-ion storage, the material shows a modified lithium reaction mechanism, which has been rationalized by the occurrence of cationic vacancies acting as additional lithium hosting sites within the anatase framework. Finally, the material shows a fast discharging/charging behavior, compared to TiO 2, highlighting the benefits of the structural modifications and paving the way for the design of advanced electrode materials, based on a defect mediated mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".