Cellulose consolidated with polyethylene glycol: The nanoscale mechanisms revealed by hybrid Monte Carlo/molecular dynamics modeling
Bibliographic record
Abstract
Polyethylene glycol (PEG) consolidation treatment is a widely used conservation strategy for wooden culture relics. However, the consolidation mechanism of PEG is still open to interpretation. PEG-cellulose, the representative component of wood cell wall, interactions are governed by various coupled multi-scale mechanisms which require nano-scale investigation. In this study, a hybrid molecular dynamics and grand canonical Monte Carlo (MD/GCMC) simulation combined with rule of mixture (RoM) analyses are employed to reveal the underlying mechanisms of PEG-induced consolidation. We found that PEG200 reduces moisture adsorption and swelling at museological conditions, confirming its consolidation effect. At high PEG content, a crossover behavior is identified at humid conditions (RH > 80) where excessive sorption and swelling are observed surpassing the untreated sample. The molecular modeling results are found to be consistent with experimental observations. Furthermore, the structural and mechanical properties of the hydrated samples are assessed by examining the porosity distribution, mechanical properties, and hydrogen bonding network. Results indicate mechanical softening induced by PEG treatment. A modified mixture model is proposed based on molecular modeling results that incorporate sorption and swelling coupling, porosity filling and mechanical softening behaviors. Two key mechanisms are identified explaining the consolidation effect of PEG: first, the PEG fills the porosities of amorphous structure thus diminishing sorption sites; second, the polymer structure prohibits PEG from further swelling thus constraining water sorption. The model and theoretical framework can serve as a guide for the design of novel consolidant materials by identifying the key molecular features of an ideal consolidant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".