Optical transparency of paper as a function of moisture content with applications to moisture measurement
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".