The Thermal-Oxidation Behavior of Pristine and Doped Magnéli Phase Titanium Oxides
Bibliographic record
Abstract
Magnéli phase titanium oxides (Ti n O2n−1, 4 ≤ n ≤ 10) are important materials for solid state and electrochemical technologies such as memristors, batteries, fuel cells, and electrochemical devices for water treatment. Developing an understanding of transitions between Ti n O2n−1 and its product of oxidation, titanium(IV) oxide (TiO2), as well as strategies such as doping to modulate the conditions for such changes will enable the development of more effective devices. To elucidate a mechanism for their thermal oxidation and investigate the influence of doping, the thermal-oxidation behavior in air of Ti4O7 doped with vanadium, chromium, and iron were investigated by thermogravimetric analysis (TGA). These powders prepared by high-temperature H2 reduction of dopant-containing TiO2 were characterized by scanning electron microscopy (SEM), gas adsorption analysis, X-ray fluorescence (XRF), energy-dispersive X-ray (EDX) spectroscopy, X-ray photoelectron spectroscopy (XPS), and powder X-ray diffraction (PXRD). V- and Fe-doping improved the thermal stability of Ti4O7 as evidenced by higher onset temperatures in their thermograms. Three-dimensional diffusion reaction models adequately describe the solid-state kinetics of thermal oxidation of Ti4O7 in air as demonstrated by linear model-fitting. Doping shows a mixed influence on the kinetics for thermal oxidation in air reducing both the Arrhenius pre-exponential factor and the activation energy.
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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".