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Record W4220709370 · doi:10.1177/00037028221094598

Temperature-Dependent Infrared Refractive Index of Polymers from a Calibrated Attenuated Total Reflection Infrared Measurement

2022· article· en· W4220709370 on OpenAlexafffund
Md. Shafiul Azam, Malcolm D. Ranson, Dennis K. Hore

Bibliographic record

VenueApplied Spectroscopy · 2022
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbsorbanceAttenuated total reflectionTotal internal reflectionInfraredRefractive indexInfrared spectroscopyOpticsCalibrationChemistryAnalytical Chemistry (journal)Polarization (electrochemistry)Angle of incidence (optics)Materials scienceReflection (computer programming)Absorption (acoustics)OptoelectronicsChromatographyPhysics

Abstract

fetched live from OpenAlex

We demonstrate a straightforward method by which a commonly available reference sample such as water can be used to calibrate an attenuated total internal reflection infrared absorbance measurement in order to account for the polarization of the beam incident on the internal reflecting element, and the spread of angles about the nominal angle of incidence. This enables quantitative comparison of attenuated total reflection-derived absorbance data with spectra calculated from optical constants. We then apply this calibration to the measurement of temperature-dependent absorption spectra of a polydimethylsiloxane sample. We illustrate that the extracted optical constants scale with the temperature-dependent changes in the polymer density better than the raw absorbance values on vibrational resonance.

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)0.004
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.248
Teacher spread0.234 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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