Utilizing an Improved EXAFS Structure Analysis Method to Reveal Site-Specific Bonding Properties of Ag44(SR)30 Nanoclusters
Bibliographic record
Abstract
Atomically precise nanoclusters (NCs) are of great interest due to their well-defined structures and molecule-like properties. Understanding their structure-property relationship is an important task because it can help tailor their structures to achieve specific desired properties. In this study, the temperature-dependent bonding properties of Ag44(SR)30 have been revealed by the extended X-ray absorption fine structure (EXAFS) with a new structure analysis method, which includes two Ag-S and two Ag-Ag fitting shells. It has been proven that the EXAFS fitting quality can be improved significantly compared with the conventional method. New insights into Ag-S bondings were discovered based on the fitting results obtained from the new method. It allows us to observe two different bonding properties within the Ag-S motifs, which cannot be discovered by using the conventional method. Additionally, the metal core of Ag44(SR)30 exhibits uncommon thermal behavior, which could be connected to the absence of the center atom in the icosahedral core. Our results demonstrate that the new structure analysis method can provide a more reliable comparison of NCs structural changes than the conventional method and it could be applicable to other NCs. The revealed temperature-dependent bonding properties can provide insights into the structure-property relationship of Ag44(SR)30, which can help design new NCs materials with tailored properties.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".