Drying stresses in cellulose nanocrystal coatings: Impact of molecular and macromolecular additives
Bibliographic record
Abstract
The industrial implementation of cellulose nanocrystals (CNCs) in films and coatings requires thorough evaluation of the internal stresses post-consolidation, as they cause fracturing and peeling. Characterizing the impact of plasticizing additives on stress is therefore critical. Herein, we use the deflection of thin glass substrates to measure drying stresses in consolidating CNC films, and benchmark the impact of five additives (glucose, glycerol, poly(ethylene glycol) (PEG), poly(vinyl alcohol) (PVA) and bovine serum albumin). Glycerol and PEG reduced drying stresses effectively, while PEG of increased molecular weight (from 0.2 to 10 kDa), PVA, and BSA were less effective. We analyzed the temporal aspects of the process, where stress relaxation of up to 30 % was observed 2 years after coating formation. Finally, we provide a framework to evaluate the impact of CNC morphology on residual stresses. The introduced approach is expected to fast-track the optimization and implementation of coatings based on biocolloids.
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) | 0.000 | 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) | 0.001 | 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".