Band-gap engineering in TiO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mrow/><mml:mn>2</mml:mn></mml:msub></mml:math>-based ternary oxides
Bibliographic record
Abstract
The electronic structure of several ternary oxides (Sn${}_{2}$TiO${}_{4}$, PbTiO${}_{3}$, Bi${}_{2}$Ti${}_{4}$O${}_{11}$, and Bi${}_{4}$Ti${}_{3}$O${}_{12}$) based on binary lone-pair oxides (SnO, PbO, and Bi${}_{2}$O${}_{3}$) and a $d$${}^{0}$ oxide (TiO${}_{2}$) is investigated using soft x-ray spectroscopy and electronic-structure calculations. We find that the valence band of these ternary oxides is bounded by bonding (at the bottom of the valence band) and antibonding (at the top of the valence band) O 2p lone-pair $ns$ (Sn $5s$, Pb $6s$, Bi $6s$) hybridized states, while the conduction band is dominated by unoccupied Ti $3d$ states. The existence of these two features is found to be independent of crystal structure or stoichiometry. The calculated hybridization in the bonding O $2p$ lone-pair $ns$ states is in reasonable agreement with the relative intensity of this feature in the measured x-ray emission spectra. The dominant influence on the conduction and the valence bands in the ternary oxides is due to different aspects of the electronic structure in the parent binary oxides, and we consequently find that the band gap of the ternary oxide is found to be a stoichiometric-weighed addition of the band gaps of the parent oxides.
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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) | 0.002 | 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".