Application of Biobased Phenol Formaldehyde Novolac Resin Derived from Beetle Infested Lodgepole Pine Barks for Thermal Molding of Wood Composites
Bibliographic record
Abstract
In this study, wood particles were thermally molded into composites using a novolac resin derived from beetle infested lodgepole pine barks with three different resin-to-wood filler weight ratios (3:7, 5:5, and 7:3). Control composites were made using a lab synthesized novolac resin without bark for comparison. Results showed that mechanical properties of the composites varied with the resin-to-filler ratios. Bark-derived resin improved the tensile strength of the composites at resin to filler weight ratio of 5:5. Meanwhile, at all three resin-to-filler weight ratios, the composites made using the bark-derived resin showed an improved water resistance than the control composites. However, the composites made with the bark-derived resin exhibited a slightly lower thermal stability than the control composites. This study demonstrated that bark derived novolac resins have great potential for application in thermal molding of wood composites to improve water resistance compared with novolac resins without bark components.
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.
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".