Effects of particle size on oxygen surface exchange kinetics determined by pulse isotope exchange
Bibliographic record
Abstract
• LSF64 and 8-YSZ oxygen exchange rates at 2% O 2 was determined. • PIE is a powerful technique; however, sufficiently small sample particles are essential. • Diffusion may affect kinetics even under proposed surface-limited regimes. Methods based on heterogeneous isotope exchange between the gas-phase and oxide surfaces have become popular experimental approaches for enhancing the fundamental understanding of rate-limiting elementary processes of the surface kinetics and developing materials with increased electrocatalytic activity towards oxygen reduction. This contribution explores the limitations of the pulse isotope exchange (PIE) technique, focusing on how particle size of the sample affects the reliability of derived kinetic parameters. Dense powders of varying particle sizes were evaluated for two materials with fundamentally different defect chemistries and transport properties: La 0.6 Sr 0.4 FeO 3-δ (LSF64) and (Y 2 O 3 ) 0.04 (ZrO 2 ) 0.96 (8-YSZ). The findings show that the ratio between the oxygen exchange and diffusion coefficients is crucial to determine the appropriate range of particle sizes that maintains the system within a kinetic regime governed by surface kinetics, adhering to the PIE approach prerequisites. If particles are too large, homoexchange and bulk diffusion can affect the measurements resulting in erroneous kinetic parameters.
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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 | 0.001 | 0.001 |
| 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".