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Record W2092581039 · doi:10.1103/physreve.84.041917

Wavelength-modulated differential photothermal radiometry: Theory and experimental applications to glucose detection in water

2011· article· en· W2092581039 on OpenAlexafffund
Andreas Mandelis, Xinxin Guo

Bibliographic record

VenuePhysical Review E · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of Toronto
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPhotothermal therapyAnalyteMaterials scienceRadiometryWavelengthSensitivity (control systems)Absorption (acoustics)OpticsOptoelectronicsChemistryChromatographyNanotechnologyPhysics

Abstract

fetched live from OpenAlex

A differential photothermal radiometry method, wavelength-modulated differential photothermal radiometry (WM-DPTR), has been developed theoretically and experimentally for noninvasive, noncontact biological analyte detection, such as blood glucose monitoring. WM-DPTR features analyte specificity and sensitivity by combining laser excitation by two out-of-phase modulated beams at wavelengths near the peak and the base line of a prominent and isolated mid-IR analyte absorption band (here the carbon-oxygen-carbon bond in the pyran ring of the glucose molecule). A theoretical photothermal model of WM-DPTR signal generation and detection has been developed. Simulation results on water-glucose phantoms with the human blood range (0-300 mg/dl) glucose concentration demonstrated high sensitivity and resolution to meet wide clinical detection requirements. The model has also been validated by experimental data of the glucose-water system obtained using WM-DPTR.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.334
Teacher spread0.320 · 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

Citations24
Published2011
Admission routes2
Has abstractyes

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Same venuePhysical Review ESame topicSpectroscopy Techniques in Biomedical and Chemical ResearchFrench-language works237,207