The Structure and Bonding of Au<sub>25</sub>(SR)<sub>18</sub> Nanoclusters from EXAFS: The Interplay of Metallic and Molecular Behavior
Bibliographic record
Abstract
While recent advances in the synthesis and crystallography of gold–thiolate nanoclusters have led to enormous progress in understanding the structure and bonding of gold thiolate nanomaterials, the changes in structure experienced by these nanomaterials in different conditions, such as low temperature and solvation, is still largely lacking. Herein, we report a temperature- and solvation-dependent extended X-ray absorption fine structure (EXAFS) study of thiolate-protected Au 25 nanoclusters. The structural changes experienced by Au 25 in response to low temperature and different solvation environments are illustrated in great detail using a site-specific EXAFS fitting approach consisting of one Au–S and three Au–Au shells. EXAFS simulations as well as ab initio calculations of the local density of states are used to support the EXAFS analysis and correlate the structural characteristics of Au 25 with its electronic properties. These experimental and theoretical studies point out the existence of the interplay between the “metallic” behavior of the Au 13 core and the “molecular” behavior of the six RS–Au–S(R)–Au–SR “staple” units within Au 25, which may shed light on its catalytic mechanism and aid in the design and synthesis of other gold–thiolate nanoclusters by balancing the metallic and molecular interactions.
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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) | 0.001 | 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".