Aging of Phosphorylated Cellulose Nanofibers under Moist-Heat Conditions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Understanding the long-term stability of cellulose nanofibers is critical for their practical application in advanced functional materials. In this study, we investigated the aging behavior of phosphorylated cellulose nanofibers (PCNFs) sheets under moist-heat accelerated aging conditions (80 °C, 65% relative humidity (RH)) for up to 42 days. PCNFs with different phosphate group densities were prepared by controlling the phosphorylation time, and their chemical and morphological changes were systematically analyzed. Liquid-sta 31 P NMR revealed a progressive transformation of surface phosphate esters into inorganic phosphate salts during aging. This dephosphorylation was thought to lead to a decrease in the pH within the sheets, which in turn promoted hydrolysis of the cellulose backbone. The resulting degradation manifested as decreases in the degree of polymerization (DP) and fibril length, particularly in PCNFs with higher surface charge. Conversely, the lateral crystallite size of the cellulose increased. These findings provide insights into PCNF aging and highlight the importance of controlling the initial phosphate ester structure and environmental conditions to increase the stability of PCNF-based materials in practical applications.
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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".