Polyurethane foams made from liquefied bark‐based polyols
Bibliographic record
Abstract
ABSTRACT Liquefaction is known to be an effective method for converting biomass into a polyol. However, the relationships between bark liquefaction conditions and properties of the resulting foams are unclear. In this study, polyurethane foams (PUF) were made using bark‐based polyols obtained through liquefaction reactions of bark at two different temperatures (90 and 130°C). Through systematic characterization of the PUFs the influence of the liquefied bark and liquefaction conditions on foam properties could be observed. The bark‐based foams had similar foaming kinetics, thermal stability, and glass transition temperatures compared with the PEG‐based control foam. The bark‐based PUF from the polyol obtained at the higher liquefaction temperature showed comparable specific compressive strength to the PEG‐based control foam. Lastly, both bark foams exhibited a high amount of open‐cell content, with the foam made from the lower temperature liquefied polyol having poor cell morphology. This deviation from the controls in the open‐cell content may explain the lower modulus values observed in the bark PUFs due to the lack of cell membrane elastic stretching as a strengthening mechanism. These results demonstrated the influence of the bark liquefaction conditions on foam properties, thereby providing a better fundamental understanding for the practical application of bark‐based PUFs. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40599.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".