Water sorption hysteresis in wood: III physical modeling by molecular simulation
Bibliographic record
Abstract
Abstract Molecular simulation has been successfully applied to sorption and hysteresis studies of various nanoporous materials, revealing underlying mechanisms that neither theoretical nor experimental approaches can achieve. In this work, the grand canonical Monte Carlo approach is used in a simplified wood-water system to simulate sorption isotherms and hysteresis at 25°C and 40°C. Wood is represented by a cell wall model composed of a solid substance and evenly distributed independent cylindrical nanopores with diameters in the range of 0.6–2.2 nm. Polysaccharides and lignin pore-wall compositions are considered. Hydroxyl groups are modeled as negative energy pits attached to walls and water is represented by the extended simple point charge model. Capillary condensation in the wide hygroscopic range and metastable states are well demonstrated in the simulations, thus supporting the independent domain model discussed in the first paper of this series. The size of simulated hysteresis loops increases with pore size, less hydrophilic lignin composition and reduced temperature. The trends shown by the model are consistent with experimental findings. The larger hysteresis can be explained by more metastable states due to weaker wall-water interaction or smaller thermal fluctuation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".