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Record W2016885443 · doi:10.1063/1.1521545

Mirage effect spectrometry and light profile microscopy: Two views of an optical depth profile (abstract)

2003· article· en· W2016885443 on OpenAlexaff
J. F. Power, Sipei Fu, Oleg Nepotchatykh

Bibliographic record

VenueReview of Scientific Instruments · 2003
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceOpticsLuminescencePhotothermal therapyScatteringLaserMicroscopyOptical microscopeBeam (structure)OptoelectronicsScanning electron microscopePhysics

Abstract

fetched live from OpenAlex

Photothermal depth profiling techniques are well adapted for the inspection of optically absorbing features on the length scale of 1–100 μm in a variety of media. However, the depth profiling mechanism intrinsic to thermal wave imaging is inherently ill posed [J. F. Power, AIP Conf. Proc. 463, 3 (1999)], and suffers obvious disadvantages such as sensitivity to experimental errors (requiring regularization) and subsurface broadening of the regularized depth profiles. Recently, through the introduction of light profile microscopy (LPM) an alternate method of optical inspection was made available for depth profiling optically absorbing, scattering, and luminescent structures on this length scale [J. F. Power and S. W. Fu, Appl. Spectros. 53, 1507 (1999); J. F. Power and S. W. Fu, U.S. Patent Pending]. LPM inspects a thin film under test by directing a laser beam through the material along the depth axis, parallel to a polished cross-sectional viewing surface. Luminescence and elastic scatter excited in the beam volume is imaged by a microscope aligned orthogonal to the beam axis. The images obtained by this method showed striking depth contrast in a variety of materials with subsurface interfaces and depth variations of luminescence yield. When implemented in dual beam mode [J. F. Power and S. W. Fu, U.S. Patent Pending; J. F. Power and S. W. Fu, (unpublished)] with an associated mathematical method, LPM may be used to quantitatively resolve depth variable optical absorption from light scattering and luminescence efficiency. In contrast to photothermal methods, the LPM technique is well posed. LPM was evaluated in tandem with mirage effect spectrometry (in normal deflection mode with bicell detection) [J. F. Power, S. W. Fu, and M. A. Schweitzer, Appl. Spectros. 54, 110 (2000)], to determine the effective use of each technique in analysis problems on complex materials. This study used samples with known depth variations of optical properties including homogeneous absorber layers, and structures composed of thin laminate assemblies of photodegraded polymers which could be disassembled and independently studied using UV-visible spectrophotometry. LPM shows consistent high sensitivity to sharp interfaces and suffers no degradation of spatial resolution with depth, while photothermal depth profiling shows substantial resolution loss. However, photothermal depth profiling exhibits a number of complementary advantages. These include a substantially enhanced sensitivity for depth profiling optical absorption (over LPM), and insensitivity to isolated optical defects and moderate levels of light scattering. The photothermal depth profiling method also had superior spatial averaging properties, which presented a smoothed picture of profiles containing regions of spurious, enhanced absorption or scattering, to which LPM is sensitive. Currently, inversion techniques based on the generalized singular value decomposition are being considered to evaluate the joint information available from both optical and photothermal probes. A full discussion of the relevant instrumental and mathematical issues will be presented.

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 score0.095
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.272
Teacher spread0.262 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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