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Record W2339361121 · doi:10.1063/1.4942251

Optical transparency of paper as a function of moisture content with applications to moisture measurement

2016· article· en· W2339361121 on OpenAlexafffund
Amir Farzad Forughi, Sheldon Green, Boris Stoeber

Bibliographic record

VenueReview of Scientific Instruments · 2016
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsWater contentSoftwoodTransparency (behavior)HardwoodMaterials scienceMoistureRefractive indexKraft paperComposite materialAbsorption of waterPapermakingProcess engineeringPulp and paper industryEnvironmental scienceComputer scienceOptoelectronicsGeotechnical engineeringGeologyEngineeringBotany

Abstract

fetched live from OpenAlex

Accurate measurement of the moisture content of paper is essential in papermaking and is also important in some paper-based microfluidic devices. Traditional measurement techniques provide very limited spatiotemporal resolution and working range. This article presents a novel method for moisture content measurement whose operating principle is the strong correlation between the optical transparency of paper and its moisture content. Spectrographic and microscopic measurement techniques were employed to characterize the relation of moisture content and relative transparency of four types of paper: hardwood chemi-thermomechanical pulp paper, Northern bleached softwood kraft paper, unbleached softwood kraft paper, and General Electric(®) Whatman™ grade 1 chromatography paper. It was found that for all paper types, the paper transparency increased monotonically with the moisture content (as the ratio of the mass-of-water to the mass-of-dry-paper increased from 0% to 120%). This significant increase in relative transparency occurred due to the refractive index matching role of water in wet paper. It is further shown that mechanical loading of the paper has little impact on the relative transparency, for loadings that would be typical on a paper machine. The results of two transient water absorption experiments are presented that show the utility and accuracy of the technique.

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 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 score0.389
Threshold uncertainty score0.248

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 head0.029
GPT teacher head0.230
Teacher spread0.201 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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