Fractal Modeling of Moisture Diffusion in Wood Cell Wall
Bibliographic record
Abstract
Abstract The mechanisms of moisture diffusion in wood are not yet fully understood, due to the complex and hierarchical structure of the wood cell wall constituents. To investigate this mechanism in this hierarchical structure, fractal geometry analysis was used as proper tool. The objective of this study is to develop a theoretical fractal moisture diffusion model for wood cell wall by taking into consideration its structural geometry and to upscale that model to gross wood by employing electrical resistance modeling and validation. The proposed fractal diffusion model is a function of fractal dimensions, porosity, and pore size distribution of the wood cell wall as well as ambient conditions such as moisture content, temperature, and inverse slope of the sorption isotherm. The water vapor sorption data that was used to drive the experimental diffusion coefficient of various wood types were studied using the dynamic sorption method. Mercury intrusion porosimetry was used to explore the detailed structural parameters of wood cell wall pore size distributions and calculate the pore fractal dimension. The derived fractal diffusion model was validated using experimental and data calculated by a past published model. The trends for diffusion coefficients predicted by the fractal model were similar to the experimental and calculated data and successfully predicted the diffusion coefficients at low moisture contents. Pore size ratio, pore, and tortuous fractal dimensions were negatively correlated to fractal diffusivity, while the porosity was positively correlated. The findings of this study contribute to the creation of a decision support system that would allow predicting wood geometric properties and moisture diffusivity properties based on wood structural and ultrastructural attributes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".