Electronic Conductivity and Stability of Doped Titania (Ti<sub>1−<i>X</i></sub>M<i><sub>X</sub></i>O<sub>2</sub>, M = Nb, Ru, and Ta)—A Density Functional Theory-Based Comparison
Bibliographic record
Abstract
The structure, electrical conductivity, and stability of Nb-, Ru-, and Ta-doped titania were compared by density functional theory. Both anatase and rutile structures were investigated. Doping causes lattice expansion in all cases. The mechanism by which Ru-doping induces electrical conductivity in titania differs from those by Ta- and Nb-doping. Ru-doping fills the titania band gap primarily with its own d-electrons. On the other hand, Ta- and Nb-doping shift the Fermi level to the originally unfilled conduction states. Substitution free energy calculations indicate that a uniform Ti 0.75 M 0.25 O 2 solution is favorable for Nb- and Ta-doping but unfavorable for Ru-doping. In addition, we also considered the effect of dopant concentration on the electrical conductivity of doped titania in the rutile phase. For Nb- and Ta-doping, increasing dopant concentration above mole fractions of 0.0625 and 0.125, respectively, gives diminished increment in Fermi level electron density. On the other hand, electron density at the Fermi level of Ru-doped rutile is more linearly dependent on Ru mole fraction.
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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) | 0.001 | 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".