<i>In Situ</i> Exsolved Metal Nanoparticles: A Smart Approach for Optimization of Catalysts
Bibliographic record
Abstract
Heterogeneous supported metal nanoparticles (NPs) are extensively applied in a variety of chemical and energy conversion processes. Traditionally, these catalysts are prepared by deposition methods. However, they usually show wide ranging size distributions and are easily subject to poisoning and coarsening or agglomeration during the reactions. Alternatively, the time and cost-effective in situ exsolution strategy has successfully addressed the above drawbacks and is able to produce finer and more evenly distributed metal NPs even at relatively low metal loading. Endowed by their socketed nature, the exsolved metal NPs possess excellent operational stabilities as well as great catalytic activities. Moreover, these exsolved metal NPs are smart and can be regenerated upon redox treatments, further extending the lifetime of catalysts. This review presents a general idea in facilitating the degree of exsolution from various oxide substrates by summarizing the recent advances in the exsolution related studies and research outputs with a special emphasis on the understanding of the thermodynamical roles of different experimental parameters.
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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) | 0.000 | 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".