Thermal Stability of Cellulose Nanomaterials
Bibliographic record
Abstract
Thermal stability is a crucial property of materials, especially when they have a wide range of thermally sensitive applications. Cellulose nanomaterials (CNMs) extracted from cellulosic biomass have garnered significant attention due to their abundance, biodegradability, sustainability, production scalability, and industrial versatility. To explore the correlation between the structure, chemistry, and morphology of CNMs and their thermal stability, we present a comprehensive literature review. We identify five major factors affecting CNMs' thermal stability, namely type, source, reaction conditions, post-treatment, and drying method, and analyze their impact on CNMs' thermal stability using several case studies from the literature. Using multiple linear least-squares regression (MLR), we establish a quantitative relationship between thermal stability and seven variables: crystallinity index of the source, dissociation constant of the reactant used, reactant concentration, reaction temperature, reaction time, evaporation rate, and post-treatment presence. By understanding these interdependencies, our statistical analysis enables the design of CNMs with predictable thermal properties and identification of optimal conditions for achieving high thermal stability. The results of our study provide crucial insights that can guide the development of CNMs with enhanced thermal stability for use in a variety of industrial applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".