Interaction Effects between Cellulose and Water in Nanocrystalline and Amorphous Regions: A Novel Approach Using Molecular Modeling
Bibliographic record
Abstract
The hydrophilic/hydrophobic nature of cellulose is based on its structural anisotropy. Cellulose chains are arranged in a parallel manner and are organized in sheets stabilized by interchain OH–O hydrogen bonds, whereas the stacking of sheets is stabilized by both van der Waals (vdW) dispersion forces and weak CH–O hydrogen bonds. Cellulose has a strong affinity to itself and materials containing hydroxyls, especially water. Based on the preponderance of hydroxyl functional groups, cellulose polymer is very reactive with water. Water molecular smallness promotes the reaction with the cellulose chains and immediately formed hydrogen bonds. Besides that, vdW dispersion forces play an important role between these two reactive entities. They stabilize the cellulose structure according to the considerable cohesive energy in the cellulose network. Hydrogen bonding, electrostatic interactions, and vdW dispersion forces play an important role in determining the cellulose crystal structure during the cellulose‐water interactions. As a result of these interactions, the volume of cellulose undergoes a meaningful change expressed not only by an exponential growth in amorphous regions, but also by an expansion in nanocrystalline regions. In addition, the volume change is associated with the swelling material expressed as a weight gain of the cellulose polymer. Molecular modeling using Accelrys Materials Studio allowed us to open a new horizon and is helpful for understanding cellulose‐water interactions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".