New Opportunity for <i>in Situ</i> Exsolution of Metallic Nanoparticles on Perovskite Parent
Bibliographic record
Abstract
One of the main challenges for advanced metallic nanoparticles (NPs) supported functional perovskite catalysts is the simultaneous achievement of a high population of NPs with uniform distribution as well as long-lasting high performance. These are also the essential requirements for optimal electrode catalysts used in solid oxide fuel cells and electrolysis cells (SOFCs and SOECs). Herein, we report a facile operando manufacture way that the crystal reconstruction of double perovskite under reducing atmosphere can spontaneously lead to the formation of ordered layered oxygen deficiency and yield segregation of massively and finely dispersed NPs. The real-time observation of this emergent process was performed via an environmental transmission electron microscope. Density functional theory calculations prove that the crystal reconstruction induces the loss of coordinated oxygen surrounding B-site cations, serving as the driving force for steering fast NP growth. The prepared material shows promising capability as an active and stable electrode for SOFCs in various fuels and SOECs for CO2 reduction. The conception exemplified here could conceivably be extended to fabricate a series of supported NPs perovskite catalysts with diverse functionalities.
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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) | 0.002 | 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".