Synthesis of Ag-NiO Nanocomposite Using Persea Americana Peel Extract and Evaluation of Its Antibacterial and Antioxidant Capacity
Bibliographic record
Abstract
Canola meal as a by-product from the vegetable oil production provides a protein-rich material which is available in large quantities but with limited areas for application. The objective of this study was to investigate the possibility of utilizing canola meal adhesive for the production of wood fiber insulation boards (WFI) using the hot-air/hot-steam-process. WFI with two different thicknesses (40/60 mm) and different densities (110/140/160/180 kg/m³) were manufactured. The testing focused on their physical-mechanical properties such as internal bond strength (IB), compressive strength (CS) and short-term water absorption (ST-WA) measured according to European standards. For a better understanding of the material and curing dynamics, the canola meal was analyzed on its protein content, lignin and pentosane content as well as its extractives content using hot water, cold water and successive extraction. Using a canola meal based adhesive resulted in promising results for IB and CS up to density of 140 kg/m³. Nonetheless, there is place for improvement for the ST-WA.
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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".