Enhancing Perovskite Electrocatalysis of Solid Oxide Cells Through Controlled Exsolution of Nanoparticles
Bibliographic record
Abstract
Perovskite oxides have received a great deal of attention as promising electrodes in both solid oxide fuel cells (SOFCs) and solid oxide electrolyzer cells (SOECs) because of their reasonable reactivity, impurity tolerance, and tunable properties. In particular, exploration is still required for improving perovskite electrodes, which normally suffer from slow kinetics in electrocatalysis. Experimental studies have led to the development of new classes of perovskites with advanced characteristics and electrode kinetics at technical levels. In parallel with those developments, achievements in theoretical and computational studies have led to substantial understanding, at the atomic level, of their physicochemical properties and electrocatalytic behaviors. Their chemical and structural flexibilities enable perovskites to accommodate most metallic elements without destroying their complex matrix structures, thereby delivering a pathway to engineer their catalytic properties. In this Minireview, recent advances in perovskite electrodes are introduced, and perovskites with exsolved nanoparticles are discussed as enhanced electrocatalytic materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".