Grafting of poly (methyl acrylate) onto sulfite pulp fibers and its effect on water absorbance
Bibliographic record
Abstract
Abstract To prepare super water absorbent hydrogels of wood cellulose fibers, poly (methyl acrylate) (PMA) was copolymerized onto softwood sulfite pulp fibers using free radical initiator followed by alkaline hydrolysis. Ceric ammonium nitrate (CAN) was used as the free radical initiator. Effects of various parameters such as fiber concentration, monomer/pulp (M/pulp) ratio, CAN concentration, and reaction time on the grafting yield and on other grafting parameters were investigated. The graft conversion was the same from low to medium fiber concentration. The amount of initiator required was found to be independent of fiber concentration to achieve maximum grafting yield. Different fiber fractions (classified based on their length) have no effect on the grafting yield. The evidence of graft copolymerization was determined by using ATR‐IR spectroscopy. The X‐ray diffraction (XRD) analysis shows that grafting takes place both in amorphous and crystalline regions of cellulose fibers and the decrease in crystallinity of the grafted fibers with an increase in grafting yield was confirmed. The surface morphology of the PMA‐g‐cellulose was characterized by scanning electron microscopy (SEM). The water retention value of the hydrolyzed grafted pulp was determined based on a centrifugation technique. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".