Synthesis and Characterization of Bio-Based Phenol-Formaldehyde Resol Resins from Bark Autoclave Extractives
Bibliographic record
Abstract
Abstract With the growing concern for fossil fuel depletion and environmental carbon footprint, there is a strong interest in exploring the renewable biomass materials as substitutes for petroleum-based feedstock. In this study, bark autoclave extractives from the mountain pine beetle (Dendroctonus ponderosae Hopkins)–infested lodgepole pine (Pinus contorta Dougl.) were used for partially replacing petroleum-based phenol in the phenol-formaldehyde (PF) resol resin synthesis. The structural characteristics of the bark autoclave extractives were examined using liquid-state 13C nuclear magnetic resonance (NMR). The curing behavior and curing kinetics, bonding strength, and bond development of the resulting bio-based bark extractive–PF resol resins were investigated using differential scanning calorimetry (DSC), lap shear, and dynamic mechanical analysis (DMA) tests, respectively. Results showed that bark autoclave extractives were a complicated mixture containing tannin, degraded hemicellulose, and degraded lignin components. The bark extractive–PF resins exhibited a higher molecular weight, higher viscosity, shorter gel time, and faster curing rate than the laboratory-made PF resin without bark components. The bark extractive–PF resins had comparable bonding strength to a commercial PF resin even when the phenol replacement rate was as high as 50 percent by weight. Bark autoclave extractives obtained from the beetle-infested lodgepole pine are suitable as a partial replacement of petroleum-based phenol in making PF resol adhesives.
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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".