Advanced Support Materials and Interactions for Atomically Dispersed Noble‐Metal Catalysts: From Support Effects to Design Strategies
Bibliographic record
Abstract
Abstract Indisputably, noble‐metal single atom catalysts (SACs) are one of the most popular research topics in the field of catalysis because of their low cost, ultrahigh atomic utilization, and distinctive performance for a wide variety of catalytic reactions. Support materials play a vital role in the preparation and catalytic performance of noble‐metal SACs. Thus, diverse support materials have been developed very rapidly and elaborately designed in the last few years. In this review, the support effects in noble‐metal SACs are first systematically introduced, including anchoring effects, strong metal–support interactions, and synergistic catalysis effects. Moreover, the most recent advances in support materials are classified and discussed in detail with a focus on their anchoring mechanism. Importantly, design strategies for advanced supports are summarized for guiding the development and utilization of advanced support materials. To conclude possible future research directions for support materials are put forward to help overcome the current issues facing noble‐metal SACs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".