Preparation of Citric Acid-Modified Cellulose Composites and Elucidation of Their Toughening Mechanism
Bibliographic record
Abstract
Cellulose is a plant-based and highly abundant biobased resource and widely used as a filler for polymer composite materials because cellulose fillers have a high aspect ratio and high crystal modulus. Introducing high contents of cellulose fillers into polymer composites reduces the use of petroleum-derived synthetic polymers, increases the mechanical strength, and decreases the toughness due to the aggregation of fillers. In this study, we introduced hydrogen bonds between the polymer matrix and cellulose fillers. Citric acid-modified cellulose (CAC) has many carboxyl groups and forms hydrogen bonds with polymers that have hydroxy groups. The interactions between the polymer matrix and the CAC fillers were evaluated by the glass transition temperature, Fourier transform infrared spectroscopy, and a simulation study based on first-principles calculations. Noncovalent interactions between the polymer matrix and CAC fillers improved the toughness of the CAC composites and enabled mechanical recycling at a high CAC content. This study contributes to the reduced use of petroleum-derived synthetic polymers and longer lifetimes of the materials.
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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 | 0.001 |
| 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".