<i>In Situ</i> Exsolved Metal Nanoparticles: A Smart Approach for Optimization of Catalysts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it