Forming End-to-End Oligomers of Gold Nanorods Using Porphyrins and Phthalocyanines
Bibliographic record
Abstract
The illumination of aggregated metal nanospecies can create strong local electric fields to brighten Raman scattering. This study describes a procedure to self-assemble gold nanorods (NRs) through the use of porphyrin and phthalocyanine agents to create reproducibly stable and robust NR aggregates in the form of end-to-end oligomers. Narrow inter-rod gaps result, creating electric field "hot spots" between the NRs. The organic linker molecules themselves are potential Raman-based optical labels, and the result is significant numbers of Raman-active species located in the hot spots. NR polymerization was quenched by phospholipid encapsulation, which allows for control of the polydispersity of the aggregate solution, to optimize the surface-enhanced Raman scattering (SERS) enhancement and permitted the aqueous solubility of the aggregates. The increased presence of Raman-active species in the hot spots and the optimizing of solution polydispersity resulted in the observation of scattering enhancements by encapsulated porphyrins/phthalocyanines of up to 3500-fold over molecular chromophores lacking the NR oligomer host.
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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) | 0.000 | 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".