A model for interpreting depth profiles of confocal Raman measurements in reflective and transmitting materials
Bibliographic record
Abstract
Abstract We present a model and experimental evaluation of the depth profiles of total intensity in confocal Raman microscopy. The model assumes a Gaussian‐like beam for excitation and Raman emission to obtain a general description of the depth profile from an arbitrary sample. For samples that emit from the surface (i.e., that do not transmit light at the excitation wavelength), this model simplifies to a Lorenzian depth profile from which Rayleigh range can be extracted by the half‐width at half maxima. We extend the model to the case of transparent samples that offer significant refractive index mismatch across the surface. We show that in these cases, the axial increase of depth of focus can be approximated by two Gaussian foci, one at the paraxial focus and one at an oblique focus. This model accurately describes experimentally observed depth profiles of reflective samples and transparent samples. We further extend the analysis to the case of thin transparent films to demonstrate that the model can be used in conjunction with physical measurements to produce accurate measurements of film thickness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".