Controlling the Degree of Polymerization, Bond Lengths, and Bond Angles of Plasmonic Polymers
Bibliographic record
Abstract
Plasmonic polymers present an interesting concept that builds on the analogy between molecular polymers and linear chains of strongly interacting metal nanoparticles. Ensemble-averaged optical properties of plasmonic polymers are strongly influenced by their structure. In the present work, we formed plasmonic polymers by using solution-based assembly of gold nanorods (NRs) end-tethered with photoactive macromolecular tethers. By using postassembly ligand photo-cross-linking, we established a method to arrest NR polymer growth after a particular self-assembly time, and in this manner, using kinetics of step-growth polymerization, we achieved control over the average degree of polymerization of plasmonic polymers. Photo-cross-linking of ligands also enabled control over the internanorod distance and resulted in the increased rigidity of NR chains. These results, along with a higher structural integrity of NR chains, can be utilized in plasmonic nanostructure engineering and facilitate advanced applications of plasmonic polymers in sensing and optoelectronics.
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 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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".