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Record W2180899145 · doi:10.1063/1.4935308

An absolute calibration method of an ethyl alcohol biosensor based on wavelength-modulated differential photothermal radiometry

2015· article· en· W2180899145 on OpenAlexafffund
Yi Jun Liu, Andreas Mandelis, Xinxin Guo

Bibliographic record

VenueReview of Scientific Instruments · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiosensorCalibrationMaterials scienceRadiometryPhotothermal therapyOpticsWavelengthEthanolSIGNAL (programming language)Absorption (acoustics)Sensitivity (control systems)AlcoholOptoelectronicsChemistryPhysicsNanotechnologyComputer science

Abstract

fetched live from OpenAlex

In this work, laser-based wavelength-modulated differential photothermal radiometry (WM-DPTR) is applied to develop a non-invasive in-vehicle alcohol biosensor. WM-DPTR features unprecedented ethanol-specificity and sensitivity by suppressing baseline variations through a differential measurement near the peak and baseline of the mid-infrared ethanol absorption spectrum. Biosensor signal calibration curves are obtained from WM-DPTR theory and from measurements in human blood serum and ethanol solutions diffused from skin. The results demonstrate that the WM-DPTR-based calibrated alcohol biosensor can achieve high precision and accuracy for the ethanol concentration range of 0-100 mg/dl. The high-performance alcohol biosensor can be incorporated into ignition interlocks that could be fitted as a universal accessory in vehicles in an effort to reduce incidents of drinking and driving.

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 score0.055
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.374
Teacher spread0.344 · 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

Citations6
Published2015
Admission routes2
Has abstractyes

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