Symmetry-Forbidden-Mode Detection in SrTiO<sub>3</sub> Nanoislands with Tip-Enhanced Raman Spectroscopy
Bibliographic record
Abstract
Among the techniques to reveal the chemistry, structure, and dynamics of surfaces, tip-enhanced Raman spectroscopy (TERS) occupies a unique position for the investigation of nonmetallic nanomaterials: it provides a wealth of information of Raman spectroscopy even under ambient conditions with the opportunity for spatial resolution below the diffraction limit. The high sensitivity of the optical near field to surfaces has been exploited on self-assembled monolayers on multiple occasions, and yet, the potential for the investigation of crystalline surfaces remains to be unfolded. Using strontium titanate (SrTiO 3 ) as a model system, we demonstrate that TERS does not only provide insight into surface symmetries but also activates otherwise symmetry-forbidden modes. The bulk phase of strontium titanate is Raman-inactive, and the optical far field therefore does not provide any first-order Raman signature: as a consequence, any peak in TERS configuration originates from the optical near field, confined to a few nanometers at the apex of the tip. We observe first-order Raman peaks interpreted as TO2, TO4, and LO4 phonon modes and the strong field enhancement of both infrared-active LO3 and Raman surface modes in agreement with density functional theory (DFT) calculations. The intensity enhancement of the surface modes shows the sensitivity of TERS to monitor surface relaxation effects associated with structural phase transformations into, e.g., a polar phase, and to detect surface reconstructions that are known to be crucial for photocatalytic activity.
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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".