Perovskite-Type Catalysts Prepared by Nanocasting: Effect of Metal Silicates on the Electrocatalytic Activity toward Oxygen Evolution and Reduction Reactions
Bibliographic record
Abstract
La 0.6 Sr 0.4 Fe 0.6 Mn 0.4 O 3−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 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 | 0.001 |
| 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".