Characterization of Tannin-Based Resins from the Barks of <i>Ficus platyphylla</i> and of <i>Vitellaria paradoxa</i>: Composites’ Performances and Applications
Bibliographic record
Abstract
This work investigates the physico-chemical and mechanical properties of tannins extracted from wood for composite materials manufacturing. Sustainable knowledge (in terms of physico-chemical properties and behaviours) of the material is needed to further enhance its applications. The condensed tannins extracted from the Bark of Ficus platyphylla (BFP) and the Bark of Vitellaria paradoxa (BVP) were analyzed using Matrix Assisted Laser Desorption/Ionization Time-Of-Flight (MALDI-TOF), Mass Spectroscopy and Attenuated Total Reflectance Fourier Transform Mid-InfraRed (ATR-FT MIR) spectra in the ranges 1800 cm-1 and 600 cm-1, as well as using CP MAS 13C-NMR. It was found that, these two tannins are procyanidin/prodelphinidin and made up of catechin/epicatechin, gallocatechin/epigallocatechin units, fisetinidin, galloyl and carbohydrates residues. Furthermore, BFP and BVP tannin bonded particleboard densities lie in the range recommended by NF EN 326-1994 standard. The resins also yielded good internal bond strength results of the panels, above relevant international standard specifications minimum requirements for interior-grade panels. The Transmission Electron Microscopy with Energy Dispersive X-ray Spectroscopy Analysis (TEM/ EDXA) are showing the ultrastructure and reveal that most of the resin material appears to be in an amorphous phase mainly composed of carbon/oxygen with small amounts of K, Ca and Mg. These particles have a very irregular morphology.
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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".