Surface Characterization of Cottonseed Meal Products by SEM, SEM-EDS, XRD and XPS Analysis
Bibliographic record
Abstract
The utilization of cottonseed meal products as valuable industrial materials needs to be exploited. We have recently produced water-washed cottonseed meal, total cottonseed protein, sequentially extracted water- and alkali-soluble proteins, and two residues after the total and sequential protein extractions at a pilot scale. In this work, the surface characteristics of the six cottonseed meal products were examined by scanning electron microscopy (SEM), scanning electron microscopy- energy dispersive spectrometer (SEM-EDS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The results showed that the surface properties of the six products differed from those of a commercial soy protein flour examined comparatively in this work. The compact morphology and relative-high N composition were observed in all three protein products, with greater similarity between the total protein and alkali-soluble protein. The surfaces of the two residue products were more porous with polysaccharide features. Washed cottonseed meal possessed the surface features similar to those of the residues. In the meantime, the N-associated functional groups were under-represented in the surfaces of all samples, compared to their bulk composition. Information derived from this work increased the understanding of the surface functional properties of cottonseed meal products, which would benefit their practical utilization.
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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".