Dipole-Induced Raman Enhancement Using Noncovalent Azobenzene-Functionalized Self-Assembled Monolayers on Graphene Terraces
Bibliographic record
Abstract
Graphene is a promising material in the field of interface science, especially for noncovalent functionalization, sensing, and for applications in catalysis and nanoelectronics. The noncovalent self-assembly of aromatic molecules on graphene promotes electronic coupling through π-π interactions that allows for quenching of the fluorescence of adsorbent molecules and the enhancement of their Raman spectra via graphene-enhanced Raman spectroscopy (GERS). Although recent work has explored the Raman enhancement on mono- and bilayer graphene, the layer dependence of both electronic phenomena (i.e., fluorescence quenching and Raman enhancement) has largely remained underexplored. Similarly, the effect of near-surface molecular dipoles on GERS has sparsely been examined. In this work, we employ self-assembled monolayers of azobenzene-decorated triazatriangulene molecules (AzoTATA) on graphene terraces to examine the effect of switchable molecular dipoles on the GERS effect, which occurs as a function of azobenzene photoisomerization. Furthermore, using empirical and computational methods, we present a systematic study for deriving the mechanism of GERS enhancement and fluorescence quenching on graphene terraces.
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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 | 0.001 | 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".