Grafting polyanhydride polymers to cellulose nanofibers
Bibliographic record
Abstract
Abstract Poly(ethylene-alt-maleic anhydride), PEMA, and modified PEMA with pendant poly(ethylene glycol) oligomers (PEG3, PEG10, PEG20) in anhydrous acetone were grafted onto mechanically produced cellulose microfibrils, CNF. The grafted CNF had up to 4.7 mmol/g of carboxylic acid groups from the hydrolyzed PEMA. Before and after grafting, the concentrations of individualized microfibrils were low (< 10% wt/wt). Atomic force microscopy revealed that the main CNF components were intermeshed microfibrils, microfibril bundles, and ribbons a few µm wide. Mastersizer particle size distributions were usually bimodal, with 10–20 µm and 100–200 µm peaks. We proposed the smaller peaks were individualized ribbons and the larger were flocculated ribbons and microfibrils. Based on the images of dried ribbons adsorbed on cationic glass and the shapes of aqueous ribbons sitting near the non-adhesive anionic glass, the PEMA-treated ribbons were stiffer than the PEMA-PEG grafted ribbons. Perhaps the high anhydride concentration on PEMA facilitated more crosslinking of the CNF surfaces compared to PEMA-PEG polymers with about 10 times less reactive anhydride groups. There was evidence that PEG-rich grafted polymers partially inhibited the formation of CNF aggregates in water.
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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) | 0.002 | 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".