Bio based phenolic resins and adhesives derived from forestry residues wastes and lignin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 it