Plasmon induced polymerization using a TERS approach: a platform for nanostructured 2D/1D material production
Bibliographic record
Abstract
Plasmon-induced chemical reactions have recently attracted great attention as a promising method for high efficiency light-energy conversion and proved to be useful in a wealth of different domains of chemistry and physics. One of the interesting and, so far, less explored avenues of such reactions is their potential for efficient, highly localized and controlled polymer production. Here, we present the first example of a localized, directed plasmon catalyzed polymerization process of a self-assembled monolayer on both silver and gold surfaces monitored by surface- and tip-enhanced Raman spectroscopy (SERS and TERS). As a proof-of-concept, a bi-functionalized dibenzo(1,2)dithiine-3,8-diamine (D3ATP) molecule that undergoes a well-known plasmon-induced coupling via the amino group into an azo group has been used. Initial dimerization is demonstrated using established marker bands associated with the formation of the azo group. A subsequent indicator for a polymerization reaction, the appearance of a new characteristic band, is monitored by time-dependent SERS and TERS experiments. We demonstrate that the dimerization reaction and hence, the subsequent polymerization, can be induced by a plasmonic feature, e.g. a TERS tip, at specific nanoscale locations and, at a much larger micron scale, by continuously scanning the plasmonic probe. The presented results provide the basis for designing further plasmonic catalysis experiments in general, and offer a new platform for producing ultra-thin polymer films with a defined structural dimension.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".