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Generalized Fourier-Laplace photothermal spectroscopy of optically absorbing media generated by arbitrary optical-excitation waveforms

2025· article· en· W4410314673 on OpenAlexafffund
Andreas Mandelis, Damber Thapa

Bibliographic record

VenuePhysical Review Applied · 2025
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversity of Toronto
FundersCanada Foundation for Innovation
KeywordsPhotothermal therapyExcitationSpectroscopyFourier transformMaterials scienceLaplace transformOpticsWaveformFourier transform spectroscopyPhysicsFourier transform infrared spectroscopyMathematicsMathematical analysisQuantum mechanics

Abstract

fetched live from OpenAlex

This work develops the thermal spectrum field theory in the dynamic steady state for photothermal fields under arbitrary optical excitation, utilizing a combined Fourier-Laplace formalism subject to generalized conductive and radiative boundary (surface) conditions. The theory represents a generalization of the thermal field spectral theory of opaque solids to photothermal spectroscopy of optically nonopaque media. The developed formalism introduces infrared thermophotonic emission as the spectrally resolved response of condensed media while eliminating signal background variations caused by incoherent (``decoherent'') leaky degrees of freedom inherent to diffusion physics. The theory is applied to ink-water mixtures with varying optical absorption coefficients. The Fourier-Laplace approach to photothermal spectroscopy demonstrates the absence of decoherent thermal energy superposition in the photothermal field, validates the dynamic steady-state nature of the recorded transient thermal waves, and enables accurate measurements of optical absorption, thermophotonic emission, and surface heat transfer coefficients.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.005
GPT teacher head0.241
Teacher spread0.236 · 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 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

Citations2
Published2025
Admission routes2
Has abstractyes

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