Deducing the Adsorption Geometry of Rhodamine 6G from the Surface-Induced Mode Renormalization in Surface-Enhanced Raman Spectroscopy
Bibliographic record
Abstract
Surface-enhanced Raman spectroscopy probes adsorbates on a plasmonic substrate and offers high sensitivity with molecular identification capabilities. In this study, we present a refined methodology for considering the supporting substrate in the computation of the Raman spectra. The supporting substrate is taken into account by employing a periodic slab model when doing the geometry optimization and normal mode analysis, and then the Raman spectrum is calculated for the isolated molecule but with the normal modes from the surface structure. We find that the interaction with the surface induces internal distortion in the molecule, and spectral shifts in the computed Raman spectrum. By comparing a low temperature surface-enhanced Raman spectroscopy measurement of Rhodamine 6G (R6G) with the computed Raman spectra of a series of adsorption geometries, we propose that the binding state captured in the experiment tends to possess the least internal distortion. This binding state involves upward orientation of ethylamine on R6G, and our calculations indicate that this is the lowest energy adsorption structure. Following this route, it is possible to infer both a molecular orientation and an adsorption geometry of the molecule from its Raman spectrum. Importantly, we note that, if the substrate correction is established to play a role, we also discuss that this corrected approach still has several shortcomings that significantly limit its overall accuracy in comparison with experimental spectra.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".