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Record W2964770478 · doi:10.1002/jrs.5686

A model for interpreting depth profiles of confocal Raman measurements in reflective and transmitting materials

2019· article· en· W2964770478 on OpenAlexaff
Subha Chakraborty, Tara F. Kahan

Bibliographic record

VenueJournal of Raman Spectroscopy · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of Saskatchewan
FundersNational Science Foundation
KeywordsOpticsRaman spectroscopyGaussian beamRefractive index profileGaussianParaxial approximationFocus (optics)WavelengthRefractive indexRayleigh scatteringMaterials scienceConfocalExcitationBeam (structure)Physics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.360
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2019
Admission routes1
Has abstractyes

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