Moisture-induced deformations of wood and shape memory
Bibliographic record
Abstract
Abstract Wood is known to swell substantially during moisture adsorption and shrink during desorption. These deformations may lead to wood damage in the form of cracking and disjoining of wooden components in e.g. floor or windows. Two swelling mechanisms may be distinguished: reversible swelling/shrinkage and moisture-induced shape memory effect. In the latter, wood is deformed in the wet state and afterward dried under maintained deformation, in order that wood retains its deformed shape even after the removal of the mechanical loading, called fixation. When wood is wetted again, it loses its fixation, partially regains its original shape, called recovery. These two mechanisms have their origin at the nanoscale and are modelled here using atomistic simulation and after upscaled to continuum level allowing finite element modelling. Hysteretic sorption and swelling are explained at nanoscale by the opening and closing of sorption sites in ad-and desorption, where in desorption water molecules preferentially remained bonded at sorption sites. The moisture-induced shape memory is explained by the moisture-induced activation of the interfaces between the reinforcing crystalline cellulose fibres and its matrix at nanoscale, referred to as a molecular switch. Our work aims to highlight that the understanding of sorption-induced reversible deformation and moisture-induced shape memory may play an important role in wood engineering and in building physics 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) | 0.002 | 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".