Nanostructure and interfacial mechanical properties of PEG/cellulose nanocomposites studied with molecular dynamics
Bibliographic record
Abstract
Our starting hypothesis is that Polyethylene glycol (PEG) can be utilized to mix with the biopolymers for consolidating fiber-reinforced composites without deteriorating their hygro-mechanical properties. The effect of PEG on the shear strength during pull-out of crystalline cellulose (CC) fiber out of an amorphous cellulose matrix is simulated with molecular dynamics. The interfacial shear stress shows a stick-slip behavior and is weakened with increasing moisture content. Shear strength increases at low moisture content, manifesting a slight strengthening of interfacial mechanical property due to cohesive forces exerted by the water molecules. At higher moisture content, shear strength is reduced due to breakage of the hydrogen bonds between CC and matrix by water molecules. When adding PEG, amorphous cellulose around the crystalline fiber is replaced by PEG, forming a mixture with amorphous cellulose. It is found that PEG-treated CC-AC composite maintains its shear strength and the presence of PEG does not deteriorate the dependence of the shear strength on moisture content. A shear strength model based on the number of hydrogen bonds between the fiber and the matrix is developed, which validates our initial hypothesis by unraveling the fundamental mechanisms at play. The model reveals that, although the shear strength per hydrogen bond between the fiber and PEG is lower than the shear strength per hydrogen bond between the fiber and amorphous cellulose, the final shear strength is partly compensated by an increase in the total number of hydrogen bonds with increasing PEG ratio. Since PEG reduces the moisture content in the composite at low relative humidity, PEG treated wood in museum conditions will show enhanced shear strength. The framework is a basis for further investigation of realistic archaeological wood with PEG-treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".