Direct molecular-level near-field plasmon and temperature assessment in a single plasmonic hotspot
Bibliographic record
Abstract
Tip-enhanced Raman spectroscopy (TERS) is currently widely recognized as an essential but still emergent technique for exploring the nanoscale. However, our lack of comprehension of crucial parameters still limits its potential as a user-friendly analytical tool. The tip's surface plasmon resonance, heating due to near-field temperature rise, and spatial resolution are undoubtedly three challenging experimental parameters to unravel. However, they are also the most fundamentally relevant parameters to explore, because they ultimately influence the state of the investigated molecule and consequently the probed signal. Here we propose a straightforward and purely experimental method to access quantitative information of the plasmon resonance and near-field temperature experienced exclusively by the molecules directly contributing to the TERS signal. The detailed near-field optical response, both at the molecular level and as a function of time, is evaluated using standard TERS experimental equipment by simultaneously probing the Stokes and anti-Stokes spectral intensities. Self-assembled 16-mercaptohexadodecanoic acid monolayers covalently bond to an ultra-flat gold surface were used as a demonstrator. Observation of blinking lines in the spectra also provides crucial information on the lateral resolution and indication of atomic-scale thermally induced morphological changes of the tip during the experiment. This study provides access to unprecedented molecular-level information on physical parameters that crucially affect experiments under TERS conditions. The study thereby improves the usability of TERS in day-to-day operation. The obtained information is of central importance for any experimental plasmonic investigation and for the application of TERS in the field of nanoscale thermometry.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".