Macroscopic Strain-Induced Transition from Quasi-infinite Gold Nanoparticle Chains to Defined Plasmonic Oligomers
Bibliographic record
Abstract
We investigate the formation of chains of few plasmonic nanoparticles-so-called plasmonic oligomers-by strain-induced fragmentation of linear particle assemblies. Detailed investigations of the fragmentation process are conducted by in situ atomic force microscopy and UV-vis-NIR spectroscopy. Based on these experimental results and mechanical simulations computed by the lattice spring model, we propose a formation mechanism that explains the observed decrease of chain polydispersity upon increasing strain and provides experimental guidelines for tailoring chain length distribution. By evaluation of the strain-dependent optical properties, we find a reversible, nonlinear shift of the dominant plasmonic resonance. We could quantitatively explain this feature based on simulations using generalized multiparticle Mie theory (GMMT). Both optical and morphological characterization show that the unstrained sample is dominated by chains with a length above the so-called infinite chain limit-above which optical properties show no dependency on chain length-while during deformation, the average chain length decrease below this limit and chain length distribution becomes more narrow. Since the formation mechanism results in a well-defined, parallel orientation of the oligomers on macroscopic areas, the effect of finite chain length can be studied even using conventional UV-vis-NIR spectroscopy. The scalable fabrication of oriented, linear plasmonic oligomers opens up additional opportunities for strain-dependent optical devices and mechanoplasmonic sensing.
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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".