Towards unraveling the moisture-induced shape memory effect of wood: the role of interface mechanics revealed by upscaling atomistic to composite modeling
Bibliographic record
Abstract
Abstract The moisture-induced shape memory effect (SME) is one of the most intriguing phenomena of wood, where wood can stably retain a certain deformed shape and, upon moisture sorption, can recover the original shape. Despite the long history of wood utilization, the SME is still not fully understood. Combining molecular dynamics (MD) and finite-element (FE) modeling, a possible mechanism of the SME of wood cell walls is explored, emphasizing the role of interface mechanics, a factor previously overlooked. Interface mechanics extracted from molecular simulations are implemented in different mechanical models solved by FEs, representing three configurations encountered in wood cell walls. These models incorporate moisture-dependent elastic moduli of the matrix and moisture-dependent behavior of the interface. One configuration, denoted as a mechanical hotspot with a fiber–fiber interface, is found to particularly strengthen the SME. Systematic parametric studies reveal that interface mechanics could be the source of shape memory. Notably, upon wetting, the interface is weak and soft, and the material can be easily deformed. Upon drying, the interface becomes strong and stiff, and composite deformation can be locked. When the interface is wetted again and weakened, the previously locked deformation cannot be sustained, and recovery occurs. The elastic energy and topological information stored in the cellulose fiber network is the driving force of the recovery process. This work proposes an interface behaving as a moisture-induced molecular switch.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".