Postsynthesis Racemization and Place Exchange Reactions. Another Step To Unravel the Origin of Chirality for Chiral Ligand-Capped Gold Nanoparticles
Bibliographic record
Abstract
We examine how postsynthesis nanoparticle ligand shell modifications as a general approach can help in the understanding of currently proposed mechanisms for gold nanoparticle chirality. We compare the CD response of chirally decorated mixed-monolayer-protected gold nanoparticles synthesized in situ with quasi-identical gold nanoparticles either prepared by place exchange reactions or subjected to an aqueous base, resulting in partial hydrolysis and simultaneous partial racemization. We find that the CD response at wavelengths where the free chiral ligand does not absorb strongly depends on the preparation conditions, i.e., in situ synthesis vs place exchange, and that postsynthesis racemization of the chiral ligand produces racemic nanoparticles with no CD response, i.e., no induction of a chiral bias during reductive nanoparticle formation. Considering all experimental results for the described gold nanoparticle system with a C12H24 spacer between the nanoparticle surface and chiral center, the so-called "vicinal effect" with the formation of a supramolecular assembly of the chiral moieties seems to be active. Finally, we argue that postsynthesis nanoparticle ligand shell modifications such as racemization and/or place exchange reactions are very powerful tools to unravel contributions of the different gold nanoparticle chirality mechanisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".