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Record W3026401146 · doi:10.1364/ol.394637

Non-contact reflection-mode optical absorption spectroscopy using photoacoustic remote sensing

2020· article· en· W3026401146 on OpenAlexafffund
Kevan Bell, Parsin Haji Reza

Bibliographic record

VenueOptics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsIllumisonics (Canada)University of Waterloo
FundersCentre for Bioengineering and Biotechnology, University of WaterlooMitacsUniversity of WaterlooillumiSonicsCanada Foundation for InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsMaterials sciencePhotoacoustic spectroscopyOpacityAbsorption (acoustics)SpectroscopyReflection (computer programming)Absorption spectroscopyNanosecondWavelengthLaserPhotoacoustic effectTunable diode laser absorption spectroscopyTotal internal reflectionOptoelectronicsTunable laserPhysics

Abstract

fetched live from OpenAlex

A method of remote optical absorption spectroscopy is described that utilizes the photoacoustic remote sensing detection technique. A nanosecond tunable excitation source is used to excite thermo-elastic pressure-induced elasto-optic modulations within targets across a wide wavelength range from 210 to 680 nm, providing optical absorption contrast. These modulations are read remotely as back-reflected intensity variations within a continuous-wave 1310 nm detection beam. The absorption spectra of several samples including dyes and biological macromolecules are captured with an 8 mm working distance in reflection-mode without the use of containment chambers or acoustic detection. This represents an initial investigation into the characteristics of this technique, which may facilitate optical absorption measurement within previously inaccessible sample types due to their size or opacity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.695
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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.

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

Citations9
Published2020
Admission routes2
Has abstractyes

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