Self‐Assembly and Surface Patterning of Polyferrocenylsilane‐Functionalized Gold Nanoparticles
Bibliographic record
Abstract
Chemical and topographic surface patterning of inorganic polymer-functionalized nanoparticles (NPs) and their self-assembly in nanostructures with controllable architectures enable the design of new NP-based materials. Capping of NPs with inorganic polymer ligands, such as metallopolymers, can lead to new synergetic properties of individual NPs or their assemblies, and enhance NP processing in functional materials. Here, for gold NPs functionalized with polyferrocenylsilane, two distinct triggers are used to induce attraction between the polymer ligands and achieve NP self-assembly or topographic surface patterning of individual polymer-capped NPs. Control of polymer-solvent interactions is achieved by either changing the solvent composition or by the electrooxidation of polyferrocenylsilane ligands. These results expand the range of polymer ligands used for NP assembly and patterning, and can be used to explore new self-assembly modalities. The utilization of electrochemical polymer oxidation stimuli at easily accessible potentials broadens the range of stimuli leading to NP self-assembly and patterning.
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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".