Mastering high resolution tip-enhanced Raman spectroscopy: towards a shift of perception
Bibliographic record
Abstract
Recent years have seen tremendous improvement of our understanding of high resolution reachable in TERS experiments, forcing us to re-evaluate our understanding of the intrinsic limits of this field, but also exposing several inconsistencies. On the one hand, more and more recent experimental results have provided us with clear indications of spatial resolutions down to a few nanometres or even on the subnanometre scale. Moreover, lessons learned from recent theoretical investigations clearly support such high resolutions, and vice versa the obvious theoretical impossibility to evade high resolution from a purely plasmonic point of view. On the other hand, most of the published TERS results still, to date, claim a resolution on the order of tens of nanometres that would be somehow limited by the tip apex, a statement well accepted for the past 2 decades. Overall, this now leads the field to a fundamental question: how can this divergence be justified? The answer to this question brings up an equally critical one: how can this gap be bridged? This review aims at raising a fundamental discussion related to the resolution limits of tip-enhanced Raman spectroscopy, at revisiting our comprehension of the factors limiting it both from a theoretical and an experimental point of view and at providing indications on how to move the field ahead. It is our belief that a much deeper understanding of the real accessible lateral resolution in TERS and the practical factors that limit them will simultaneously help us to fully explore the potential of this technique for studying nanoscale features in organic, inorganic and biological systems, and also to improve both the reproducibility and the accuracy of routine TERS studies. A significant improvement of our comprehension of the accessible resolution in TERS is thus critical for a broad audience, even in certain contexts where high resolution TERS is not the desired outcome.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".