Tip-Enhanced Raman Spectroscopy of Self-Assembled Thiolated Monolayers on Flat Gold Nanoplates Using Gaussian-Transverse and Radially Polarized Excitations
Bibliographic record
Abstract
Tip-enhanced Raman spectroscopy (TERS) is a highly sensitive spectroscopic technique that combines the spatial resolution of scanning near-field techniques with the chemical specificity of vibrational spectroscopy. TERS is based on the excitation of the localized surface plasmon resonance at the apex of an AFM metallized tip, producing a confined and enhanced electromagnetic field. Due to the inherent local nature of TERS and its confinement in the optical near-field of the object, TERS measurements can also be used to probe monolayers adsorbed onto surfaces providing better surface specificity in addition to higher spatial resolution. We implement here gap-mode TERS using gold nanoplates functionalized with thiolated reference molecules such as alkoxy substituted azobenzene thiol and 4-nitrothiophenol. The monolayer is probed with a silver coated AFM tip in order to obtain the largest electromagnetic field enhancement from the surface plasmon localized between the silver tip and the functionalized gold surface. More specifically, we have measured the TERS spectra of the self-assembled monolayer on gold using 532 nm excitation that is linearly (Gaussian–transverse TEM00) and radially polarized. We report the nature of the collected TERS spectra for the thiolated molecules (azobenzene thiol and nitrothiophenol) that appear to be dependent on the polarization of the excitation light at the tip/substrate interface.
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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".