Perovskite-Type Catalysts Prepared by Nanocasting: Effect of Metal Silicates on the Electrocatalytic Activity toward Oxygen Evolution and Reduction Reactions
Bibliographic record
Abstract
La0.6Sr0.4Fe0.6Mn0.4O3−δ (LSFM) perovskite-type catalysts were prepared by a nanocasting route based on solution combustion synthesis, called the soft–hard templating (SHT) approach. Three silica hard templates with different textural properties were used to increase the specific surface area of the perovskites, and hence to improve their electrochemical activity for both oxygen evolution and reduction reactions. Careful structural and physicochemical characterization revealed that the materials are composites formed by crystalline LSFM perovskite and amorphous metal silicates, even after the etching procedure. Both the composites’ specific surface area and the fraction of silicates are proportional to the silicas pore volume. Interestingly, the LSFM perovskite obtained by SHT has lower cell volumes than the parent oxide prepared without a silica template. The electrochemical characterization revealed the contrasting effects of the metal silicates on the performances of the electrodes. The LSFM-SHT-based electrodes have higher double-layer capacitance and higher current for oxygen evolution than those prepared with the parent LSFM. Furthermore, the LSFM-SHT-based electrodes show a preferential 4-electron pathway during the oxygen reduction reaction, if the amount of amorphous silicates is low (Si wt % < 3.5 wt %). However, the silicates also shift the onset potential of both oxygen evolution and reduction reactions to more positive and negative potential values, respectively, thus delaying the two reactions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".