Promoting Effect of Supports with Oxygen Vacancies as Extrinsic Defects on the Reduction of Iron Oxide
Bibliographic record
Abstract
The utilization of substrates that contain oxygen vacancies represents an effective approach to improve the reducibility of supported metal oxides and, thereby, increase the activity of metallic catalysts and the performance of oxygen carriers. However, the influence of different types of oxygen vacancies of the substrates on the reduction kinetics of supported metal oxides is not well understood. Here this influence was analyzed for supported iron oxide using temperature-programmed reduction (H 2 -TPR), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Kinetic parameters obtained by deconvolution of TPR profiles using a novel method indicate that supports containing oxygen vacancies as extrinsic defects (BaZr 0.8 Ce 0.1 Y 0.1 O 3−δ and yttria-stabilized zirconia) promote the reduction of iron oxide with activation energies lower than for the reduction of pure iron oxide. The reaction mechanism is successfully described by a nucleation and nuclei growth model (Avrami–Erofeev) in a combined two- and three-step process with the formation of metallic iron at low temperatures (∼400 °C). In contrast, supports with oxygen vacancies as intrinsic defects (CeO 2 and TiO 2 ) inhibit the reduction due to complex kinetics associated with strong metal–support interactions. The mechanism by which oxygen vacancies affect the reducibility of iron oxides is rationalized in terms of different electronic/metal–support interactions.
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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".