Eucalyptus Bark as Source of Bio-oil or Phenolic Compounds
Bibliographic record
Abstract
Background: Eucalyptus bark and scraps are generated in the production of mediumdensity fiberboard (MDF). An approach aiming to add value to such wastes was studied, following the concepts of circular economy and biomass cascade strategy. Bio-oil and phenolic resin were produced by pyrolysis from two types of biomass, Eucalyptus bark and MDF waste. As is well known, conventional phenolic resins are normally obtained from fossil resources. These products were obtained from the pyrolysis of two types of biomass to reduce environmental waste and dependence on petroleum-based products. Objective: The main objective of the present study was to produce phenolic resin from Eucalyptus wastes, aiming to reduce the fossil dependence on conventional resins used in the production line of MDF. Methods: Fast pyrolysis and slow pyrolysis were employed for bio-oil and phenolic resin production. The bio-oil and resins were characterized with standard lab analyses for their physicochemical properties, while their thermal properties were studied via thermogravimetric analysis (TGA). Results: The shear strength of the lap internal bonding of the phenolic resin binders with 19.8% of bio-oil was 2.09, 1.34, and 1.63 MPa under dry, boiler, and soaked conditions, respectively, which was acceptable for panel fabrication and can represent a significant saving in terms of fossil resins and cost reduction. Discussion: According to the results, 1 g medium fraction of bio-oils was equivalent to 1.35 g of conventional phenols, indicating those bio-oils as phenolic structures that could be used as binders. The bio-oil yields for bark and MDF were 40.9 and 25.1, respectively, which indicate a potential for replacing the conventional fossil-based phenolic resin. Conclusion: The results revealed the possibility of replacing conventional fossil-based chemicals with phenolic resin from renewable resources with similar overall properties, replacing about 1/3 of the conventional resin.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".