Sensitive X-ray Absorption Near Edge Structure Analysis on the Bonding Properties of Au<sub>30</sub>(SR)<sub>18</sub> Nanoclusters
Bibliographic record
Abstract
Abstract Au nanoclusters (NCs) with organothiolate protecting ligands are a field of great interest and X-ray absorption spectroscopy is a useful tool for the structure and property studies of these Au NCs. However, the Au NCs normally show broad and low-intensity features in the gold X-ray absorption near-edge structure (XANES) region, lowering the sensitivity of the technique and making it difficult to use for the analysis of Au NCs. In this work we report a sensitive gold L3-edge XANES study on the bonding properties of the newly discovered Au30(SR)18 NCs utilizing a combined approach of the first derivative XANES spectra and quantum simulations. First derivative XANES spectra are compared with the well-studied Au25(SR)18 with the aim of determining the unique features of Au30(SR)18. It is found that the early XANES region of the Au NCs is significantly influenced by the gold–gold bonding environment in the surface sites, as the varying surface Au–Au bond lengths in Au25(SR)18 and Au30(SR)18 result in pronounced difference in the first derivative XANES. These findings can be consistently explained using site-selective quantum simulations of the XANES spectra based on the Au NC structural models. The XANES method presented in this work offers a useful tool for the sensitive analysis on structure and bonding properties of Au NCs.
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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".