Oxidative Elimination and Reductive Addition of Thiol‐Terminated Polymer Ligands to Metal Nanoparticles
Bibliographic record
Abstract
Abstract Metal nanoparticles (NPs) stabilized with thiol‐ (HS‐) terminated polymers have applications in medicine, optoelectronics, and catalysis. It is assumed that upon exposure to oxidants or even air, these NPs lose colloidal stability, due to the oxidation of the HS‐end‐group and elimination of polymer ligands from the NP surface, however, this mechanism does not explain the unsuccessful recovery of the NP stability by adding fresh HS‐terminated polymers. Here we propose the oxidation of the surface metal atoms as a mechanism for the oxidative elimination of polymer from the NP surface. Based on this mechanism, we reversed NP aggregation by reducing the oxidized metal surface and re‐attaching HS‐terminated polymer ligands. This mechanism is general for various metal NPs and different HS‐terminated polymers. We show that oxidative elimination and reductive addition reactions can improve the colloidal stability of polymer‐capped metal NPs and control their redox stimuli‐responsive self‐assembly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".