Determination of oxygen adsorption–desorption rates and diffusion rate coefficients in perovskites at different oxygen partial pressures by a microkinetic approach
Bibliographic record
Abstract
A novel, powerful method based on a microkinetic approach is described for the estimation of the oxygen transport parameters of mixed electronic conducting materials (MIECs). This method is validated on the perovskite La0.6Sr0.4Co0.2Fe0.8O3-δ and has been applied on Ba0.5Sr0.5Co0.8Fe0.2O3-δ. This approach is original and relevant in that the surface kinetic rate constants are measured using a sample in powder form. In contrast to methods previously used, such as isotope exchange depth profiling (IEDP) and electrical conductivity relaxation (ECR), which determine the global exchange kinetic parameter, our microkinetic modelling approach allows the estimation of the forward and reverse kinetic rates accounting for the oxygen vacancy concentration. Also, the self-diffusion rate coefficient has been estimated at different oxygen partial pressures. This microkinetic approach, which combines SSITKA (steady-state isotopic transient kinetic analysis) and thermogravimetric measurements at controlled oxygen partial pressure, has the potential to significantly accelerate the characterization of oxygen transport in perovskites and related materials in the future. In this study, the kinetic parameters were measured in a temperature window between 873 K and 1173 K, and at two oxygen pressure conditions (21 kPa and 1 kPa) that are appropriate for simulating the semi-permeability of oxygen in a membrane in a process of oxygen separation from air.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".