Surface Basicity Controlled Degradation and Recoverability of Proton Conducting Perovskites, BaZr<sub>0.8</sub>Ce<sub>0.1</sub>Y<sub>0.1</sub>O<sub>3−δ</sub> and Ba<sub>0.5</sub>Sr<sub>0.5</sub>Ce<sub>0.6</sub>Zr<sub>0.2</sub>Gd<sub>0.1</sub>Y<sub>0.1</sub>O<sub>3−δ</sub>, in the Presence of CO<sub>2</sub>
Bibliographic record
Abstract
A fundamental understanding of carbonation kinetics is essential for the development of durable proton conducting electrolysis cells that can electrochemically convert CO 2 into fuels. The degradation behavior of two representative proton conducting perovskite materials, BaZr 0.8 Ce 0.1 Y 0.1 O 3−δ (BZCY811) with low cerium content and strontium-containing Ba 0.5 Sr 0.5 Ce 0.6 Zr 0.2 Gd 0.1 Y 0.1 O 3−δ (BSCZGY6211), was studied and analyzed using different solid–gas reaction models. These perovskite compositions are promising candidates for various electrochemical applications where a combination of high conductivity and high stability is required such as for direct CO 2 conversion to fuels (or syngas). The kinetic stability of these two compositions under CO 2 (and CO) was evaluated using X-ray diffraction, X-ray photoelectron spectroscopy, micro-Raman spectroscopy, CO 2 temperature-programmed desorption, and N 2 physisorption. Kinetic analysis of the isothermal solid–gas reaction indicated that BZCY811 powders were partially carbonated according to a first-order nucleation/nuclei growth mechanism, in which oxygen vacancies act as active nucleation sites. The degradation was not complete as a protective external carbonate layer was formed, which maintained the internal crystalline structure of BZCY811. In contrast, BSCZGY6211 was kinetically stable in the presence of CO 2 with no signs of degradation at high pressures (2.0 MPa). The higher kinetic stability of the latter composition is rationalized in terms of its higher surface acidity.
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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".