Towards polarization-based excitation tailoring for extended Raman spectroscopy
Bibliographic record
Abstract
Undoubtedly, Raman spectroscopy is one of the most elaborate spectroscopy tools in materials science, chemistry, medicine and optics. However, when it comes to the analysis of nanostructured specimens or individual sub-wavelength-sized systems, the access to Raman spectra resulting from different excitation schemes is usually very limited. For instance, the excitation with an electric field component oriented perpendicularly to the substrate plane is a difficult task. Conventionally, this can only be achieved by mechanically tilting the sample or by sophisticated sample preparation. Here, we propose a novel experimental method based on the utilization of polarization tailored light for Raman spectroscopy of individual nanostructures. As a proof of principle, we create three-dimensional electromagnetic field distributions at the nanoscale using tightly focused cylindrical vector beams impinging normally onto the specimen, hence keeping the traditional beam-path of commercial Raman systems. In order to demonstrate the convenience of this excitation scheme, we use a sub-wavelength diameter gallium-nitride nanostructure as a test platform and show experimentally that its Raman spectra depend sensitively on its location relative to the focal vector field. The observed Raman spectra can be attributed to the interaction with transverse and pure longitudinal electric field components. This novel technique may pave the way towards a characterization of Raman active nanosystems, granting direct access to growth-related parameters such as strain or defects in the material by using the full information of all Raman modes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".