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Record W1964198446 · doi:10.1063/1.1521507

Depth profiling the optical absorption and thermal reflection coefficient via an analysis based on the method of images (abstract)

2003· article· en· W1964198446 on OpenAlexaff
J. F. Power

Bibliographic record

VenueReview of Scientific Instruments · 2003
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Numerical Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsThermal conductivityThermal diffusivityMaterials sciencePlanarOpticsAttenuation coefficientAbsorption (acoustics)Analytical Chemistry (journal)PhysicsThermodynamicsChemistry

Abstract

fetched live from OpenAlex

The problem of depth profiling optical absorption in a thermally depth variable solid is a problem of direct interest for the analysis of complex structured materials. In this work, we introduce a new algorithm to solve this problem in a planar layered sample which is impulse irradiated. The sample is comprised of “N” model layers of thickness Δx, of constant diffusivity α, where the conductivity varies depth wise with each layer. This derivation extends to the general case of a depth variable thermal reflection coefficient with depth variable optical source density. In such a sample, at finite time, t, past excitation, thermal energy can only significantly penetrate NL model layers NL≈4αt[−ln(ε)]/2Δx, where ε is a small error (ε⩽10−6) and a double transit through each layer is assumed. The depth profile of optical absorption in each layer, i, is approximated by δ(x−iΔx), weighted by the optical source density Si. The temperature at x=0− just inside a front medium contacting the sample is given by T(x=0,t)= ∑ i=12NLSi⋅GR(x,x0=iΔx,t)]x=0, where GR(x,x0,t) represents an effective Green’s function for optical absorption at the depth x0=iΔx in the sample. The method of images1 gives GR(x,x0=iΔx,t) in the following form: [GR(x,0Δx,t)GR(x,2Δx,t)…GR(x,2NLΔx,t)]=[A10 A12 A14 A16 …..A1,2NL0 A32 A34 A36 …..A3,2NL….0……A2NL−1,2NL][G(x−0Δx,t)G(x−2Δx,t)……G(x−2NLΔx,t)]. The G(x−nΔx,t) are shifted image fields obtained from the infinite domain Green’s function for one-dimensional heat conduction. They account for thermal wave reflection/transmission over the path length nΔx from the source (at interface i) to the surface (x=0). The Ain are lumped coefficients giving the efficiency of heat transmission from the ith source to the surface for each path order n. They are determined by a mapping procedure that identifies all propagation paths of each order, n, and computes the individual and lumped reflection coefficients. Equation (2) is written for sources placed at odd ordered model interfaces. A similar upper triangular matrix results for the placement of sources at the even ordered interfaces. Recovery of the optical absorption profile proceeds by inversion of Eq. (1) for known GR(x,iΔx,t). Determination of the kernel requires a solution of the related type II inverse problem.2,3 An evaluation of this procedure and its conditioning 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.331
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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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