Controlled Environment Neutron Radiography of Moisture Sorption/Desorption in Nanocellulose-Treated Cotton Painting Canvases
Bibliographic record
Abstract
Nanocellulose-based materials have recently been used to consolidate degraded cotton painting canvases. Canvas-supported paintings consist of materials that are sensitive to moisture and especially susceptible to environmental fluctuations in temperature and relative humidity (RH). These environmental fluctuations occur in uncontrolled environments found in historic houses and palaces and can lead to hydrolytic degradation and mechanical damage to canvases. To simulate this situation in an experimental setting, canvas samples were mounted in a custom-made closed-cell and subjected to programmed cycles of RH at a controlled temperature while exposed to the neutron beam. Results are presented for both untreated samples and those treated with a polar consolidant, cellulose nanofibrils (CNF(aq)) in water, and an apolar consolidant, a composite of persilylated methyl cellulose with surface silylated cellulose nanocrystals (MC+CNC(h)) in heptane. They were then compared with changes in ionic conductivities as measured by dielectric analysis (DEA) with the same cyclic RH program and temperature. Although the samples were exposed to the same experimental conditions, they presented treatment-specific responses. CNF-treated canvas showed higher hygroscopicity than the untreated sample and facilitated moisture diffusion across the sample to areas not exposed to the environment. A sample treated with MC+CNC(h) retarded moisture diffusion during the increase in RH and could, therefore, afford protection to moisture absorption in uncontrolled environments. Thus, the experimental setup and resulting data provide a pilot study demonstrating the potential of neutron radiography in following and comparing real-time moisture diffusion dynamics in untreated and nanocellulose-consolidated cotton canvases and assisting in validating the overall benefit of the treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.008 | 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 teacher head, 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".