Comparision of Different Raw Densities and Board Thicknesses in the Production of Enzymatically Bonded Fiberboards
Bibliographic record
Abstract
Reducing CO2 emissions for the long term is an important issue in countering climate change. For this reason, the use of renewable construction materials inter alia wood-based materials is becoming increasingly important. Wood based materials often depend on petrochemical-based binding agents, most of which contain and emit formaldehyde. This causes environmental and health-related difficulties that could be rectified by avoiding the utilization of these adhesives. For this purpose, enzymatically bonded medium-density fiberboards (MDF) with a targeted raw density of 650 kg/m³ and 750 kg/m³ and high-density fiberboards (HDF) with a density of 900 kg/m³ were produced in different thicknesses. Laccase-Mediator-System (LMS) was applied to manufacture the boards and to compare them to those glued with UF and inactivated laccase, respectively. These panels were tested in terms of their physical technical properties, such as internal bond strength (IB), modulus of rupture (MOR) and thickness swelling (TS) after 24 h immersion. The aim was to show the correlation between density and these properties for fiberboards bonded this way. Most panels meet the requirements of the European standard when LMS and UF-resin were used. The possibility to produce fiberboards bonded with alternative bonding agents opens up the way to alternative, innovative, healthy and environmental-friendly wood based products.
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 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 | 0.009 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".