Bio‐oil from whole‐tree feedstock in resol‐type phenolic resins
Bibliographic record
Abstract
ABSTRACT Renewable chemicals are of growing importance in terms of opportunities for environmental concerns over fossil‐based chemicals. Lignocellulosic biomass can be converted into energy and chemicals via thermal and biological processes. Among all the transformation processes available, fast pyrolysis is the only one to produce a high yield of a liquid‐phase product called bio‐oil or pyrolysis oil. Bio‐oil is considered to be a promising substitute for phenol in phenol formaldehyde (PF) resin synthesis. In this work, bio‐based phenolic resins have been formulated, partially substituting phenol by bio‐oils from two Canadian whole‐tree species. The new resins are produced by replacing 25, 50, and 75% of phenol with bio‐oil for each species (three bioresins per species). The aim of this study is to synthesize renewable resins with competitive price and satisfactory quality. The results obtained have shown that substitution degree up to 50% provided reactivity and performance equal or superior to the pure PF resin. They also present a good storage stability, improved shear strength, and thermal stability comparable to the pure PF. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2014, 131, 40014.
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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 | 0.000 | 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) | 0.001 | 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".