Surface Characteristic of Wheat Straw Treated with Plasma
Bibliographic record
Abstract
The waxy wheat straw surface and high SiO2content of wheat straw agrifibres make wheat straw-based panel production much more difficult than traditional wood-based panel manufacture. Plasma surface modification is regarded as one of the cost effective surface treatment techniques for many materials including natural fibers. In this study, plasma technique was employed to treat the surface of wheat straw. After the plasma treatment, the surface properties were then evaluated by determining the contact angles of 3 liquids on the wheat straw surface and by analysis using Fourier Transform Infrared Spectroscopy (FTIR).The results showed that the contact angles of water, glycerol, and UF resin after the plasma treatment decreased by 44.1%, 18.6%, and 24.9%, respectively. In the meantime, –OH, and -C=O groups increased according to the FTIR analysis. The FTIR analysis also indicated a significant SiO2reduction in the plasma treated wheat straw. Obviously, the plasma treatment improved the wettability of wheat straw, increased the numbers of oxygen-containing functional groups, and also removed the weak interface of wheat straw remarkably. By means of the plasma treatment, the internal bond property between modified wheat straw fibers was expected to enhance, and thus some cheaper traditional adhesives such as urea formaldehyde (UF) and phenol formaldehyde (PF) can be used for the wheat straw-based panel production instead of using expensive Diphenylmethane Diisocyanate (MDI).
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) | 0.002 | 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".