Extraction and Characterization of Latex and Natural Rubber from Rubber-Bearing Plants
Bibliographic record
Abstract
Consecutive extraction of latex and natural rubber from the roots of rubber-bearing plants such as Taraxacum kok-saghyz (TKS), Scorzonera tau-saghyz (STS), and Scorzonera Uzbekistanica (SU) were carried out. Latex extraction was carried via two methods: Blender method and Flow method. The results of latex extraction were compared. Cultivated rubber-bearing plants contained slightly higher latex contents compared to those from wild fields. Several creaming agents for latex extraction were compared. About 50% of total natural rubber was extracted as latex. The results of the comparative studies indicated that optimum latex extraction can be achieved with Flow method. The purity of latex extracted by Blender method ( approximately 75%) was significantly lower than that extracted by Flow method (99.5%). When the latex particles were stabilized with casein, the latex was concentrated significantly. Through concentrating latex by flotation, the latex concentration of 35% was obtained. Bagasse contained mostly solid natural rubber. The remaining natural rubber in the bagasse (left after the latex extraction) was extracted using sequential solvent extraction first with acetone and then with several nonpolar solvents. Solid natural rubber was analyzed for gel content and characterized by size exclusion chromatography (SEC) for molecular weight determinations. SEC of solid natural rubber has shown that the molecular weight is about 1.8E6 and they contain less gel compared to TSR20 (Grade 20 Technically Specified Rubber), a commercial natural rubber from Hevea brasiliensis.
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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".