Bio based phenolic resins and adhesives derived from forestry residues wastes and lignin
Bibliographic record
Abstract
The work presented here aims to produce bio-phenolic compounds from forestry biomass (residues, wastes and lignin), and substitute petroleum-based phenol with the bio-phenolic compounds to produce high quality bio-based phenol formaldehyde (PF) resins. \nFor the production of bio-phenolic compounds from biomass, alcohol (methanol or ethanol) and water showed synergistic effects on biomass direct liquefaction. 65 wt% of bio-oil and a biomass conversion at > 95% were obtained at 300 ?C for 15 min in the 50%/50% (w/w) co-solvent of either methanol-water or ethanol-water. At a temperature higher than 300 ?C, conversion of bio-oil to char was significant via re-polymerization reactions. The Fourier Transform Infrared Spectroscopy (FTIR) and Gas Chromatography-Mass Spectroscopy (GC-MS) analyses of the obtained bio-oils confirmed the presence of primarily phenolic compounds and their derivatives (such as benzenes), followed by aldehyde, long-chain (and cyclic) ketones and alcohols, ester, organic acid, and ether compounds. The Gel Permeation Chromatography (GPC) results suggested that hot-compressed ethanol as the liquefaction solvent favored lignin degradation into monomeric phenols. The X-ray Diffraction (XRD) patterns of Eastern White Pine (Pinus strobus L.) wood before and after the liquefaction displayed that the cellulosic structure of the feedstock was completely converted into amorphous carbon at around 300 ?C, and into crystalline carbon at about 350 ?C.
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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".