Thermally-Degradable Thermoset Adhesive Based on a\nCellulose Nanocrystals/Epoxy Nanocomposite
Bibliographic record
Abstract
For\nhigh-value components, reusability is often an important design\nconsideration. For adhesively joined parts, the disassembly mechanism\ncan be a key factor, and in some cases the thermal degradability of\nadhesives determines the reusability and recyclability of the components.\nAfter use, components that can be easily separated are generally more\neasily reused, but this requires controlled and well-understood adhesive\ndegradation. Here, polymer nanocomposites based on epoxy resin and\ncellulose nanocrystals (CNCs) were fabricated, and their properties\nwere examined as degradable adhesives. The distribution of CNCs within\nthe epoxy resin was investigated by electron microscopy and mass spectroscopy.\nBy incorporating CNCs into epoxy matrices, the shear strength of nanocomposites\nwas improved by 31% and the effective thermal degradation temperature\nwas reduced by 40 °C. Additionally, chemical analysis by X-ray\nphotoelectron spectroscopy showed that sulfonate groups on the surface\nof CNCs play a critical role over improving the mechanical properties,\nwhile thermally induced breakage of these bonds mediates the thermal\ndegradation of the nanocomposite.
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 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) | 0.000 | 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) | 0.044 | 0.004 |
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; both teacher heads agree on what is shown here.
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".