Controlling the Structure, Properties and Surface Reactivity of Clickable Azide‐Functionalized Au<sub>25</sub>(SR)<sub>18</sub> Nanocluster Platforms Through Regioisomeric Ligand Modifications
Bibliographic record
Abstract
Abstract To fine‐tune structure–property correlations of thiolate‐protected gold nanoclusters through post‐assembly surface modifications, we report the synthesis of the o, m, and p regioisomeric forms of the anionic azide‐functionalized [Au25(SCH2CH2‐C6H4‐N3)18]1− platform. They can undergo cluster–surface strain‐promoted alkyne–azide cycloaddition (CS‐SPAAC) chemistry with complementary strained‐alkynes. Although their optical properties are similar, the electrochemical properties appear to correlate with the position of the azido group. The ability to conduct CS‐SPAAC chemistry without altering the parent nanocluster structure is different as the isomeric form of the surface ligand is changed, with the [Au25(SCH2CH2‐p‐C6H4‐N3)18]1− isomer having the highest reaction rates, while the [Au25(SCH2CH2‐o‐C6H4‐N3)18]1− isomer is not stable following CS‐SPAAC. Single‐crystal X‐ray diffraction provide the molecular structure of the neutral forms of the three regioisomeric clusters, [Au25(SCH2CH2‐o/m/p‐C6H4‐N3]0, which illustrates correlated structural features of the central core as the position of the azido moiety is changed.
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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".