Evaluation of the Adhesion and Performance of Natural Consolidants for Cotton Canvas Conservation
Bibliographic record
Abstract
Recent developments in paper and canvas conservation have seen the introduction of nanocellulose (NC) as a compatible treatment for the consolidation of historical cellulosic artifacts and manuscripts. However, as part of the assessment of these new materials for canvas consolidation, the adhesion of the consolidation treatment (which takes place between the applied material and the substrate) has not yet been evaluated, and as a result, it is poorly understood by both the scientific and conservation communities. After evaluating the potential of NC treatments for the consolidation of cotton painting canvas, we investigate a route to promote the interaction between the existing canvas and the nanocellulose treatment, which is in our case made of cellulose nanofibrils (CNF). This was carried out by introducing a cationic polymer, polyamidoamine-epichlorohydrin (PAAE), as an intermediate layer between the canvas and the CNF. The morphological, chemical, and mechanical evaluation of the canvas samples at different relative humidity (RH) levels demonstrated how the adhesion of the added PAAE layer is a dominant factor in the consolidation process. Improvement in the coating of canvas single fibers by the CNF, higher adhesion energy between the canvas fibers and the CNF treatment, and finally overall stronger canvas reinforcement were observed following the introduction of PAAE. However, an increase in mechanical response to moisture sorption and desorption was also observed for the PAAE-treated canvases. Overall, this study shows the complexity of such systems and, as such, the relevance of using a multiscale approach for their assessment.
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) | 0.001 | 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 | 0.001 |
| 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".