Reinforcement of soy polyol‐based rigid polyurethane foams by cellulose microfibers and nanoclays
Bibliographic record
Abstract
Abstract Water‐blown rigid polyurethane foams from soy‐based polyol were prepared and their structure–property correlations investigated. Cellulose microfibers and nanoclays were added to the formulations to investigate their effect on morphology, mechanical, and thermal properties of polyurethane foams. Physical properties of foams, including density and compressive strength, were determined. The cellular morphologies of foams were analyzed by SEM and X‐ray micro‐CT and revealed that incorporation of microfibers and nanoclays into foam altered the cellular structure of the foams. Average cell size decreased, cell size distribution narrowed and number fractions of small cells increased with the incorporation of microfibers and nanoclays into the foam, thereby altering the foam mechanical properties. The morphology and properties of nanoclay reinforced polyurethane foams were also found to be dependent on the functional groups of the organic modifiers. Results showed that the compressive strengths of rigid foams were increased by addition of cellulose microfibers or nanoclays into the foams. Thermogravimetric analysis (TGA) was used to characterize the thermal decomposition properties of the foams. The thermal decomposition behavior of all soy‐based polyurethane foams was a three‐step process and while the addition of cellulose microfibers delayed the onset of degradation, incorporation of nanoclays seemed to have no significant influence on the thermal degradation properties of the foams as compared to the foams without reinforcements. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
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) | 0.000 | 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".