Lignin-Laccase-Mediator-Systems (LLMS) for the Production of Binderless Medium Density Fiberboards (MDF)
Bibliographic record
Abstract
Medium density fiberboards (MDF) are produced mainly by urea-formaldehyde resins (UF) as binding agent, which are synthesized from finite fossil resources. Those boards may emit critical amounts of formaldehyde, which can influence the health of humans and animals. In recent times the wood panel board industry is looking for alternative glues which contain less or no formaldehyde. In order to avoid potential formaldehyde emissions altogether it would be preferable not to use binders which are formaldehyde based at all. One possibility is to use natural binders or to activate the wood fibers’ own binding forces by applying Laccase-Mediator-Systems (LMS). As a support of the LMS interactions with wood fibers technical lignin can be added. In this study it was found out that the addition of technical lignin intensified the fiber to fiber bindings. Two Lignin-Laccase-Mediator-Systems (LLMS) were analysed by Gel-Permeation-Chromatography and Cyclic voltammetry. Later the LLMSs were tested in the pilot scale production of MDF. The determination of the physical technological properties revealed that the LLMS treated MDF have higher dimension stabilities than only LMS treated MDF and approximately the same thickness swelling after 24 h. The results indicate that the application of LLMSs have a high potential for natural bonded MDF.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".