Impact of MTA blend % in melt spinning process and polyester properties
Bibliographic record
Abstract
Abstract In Polyethylene terephthalate (PET) processing the raw materials are purified terephthalic acid (PTA), moderately purified terephthalic acid (MTA), monoehalene glycol (MEG). The processing of PTA is very difficult and costly. For reducing PTA percent we are using different percentages of MTA blend in PET processing. MTA affected the properties of polyester and melt spinning process. The properties such as elongation, tenacity, molecular chain length, b-color and IV, thermal properties, % carboxyl groups will change with MTA percentage. MTA percentage also affects fiber breakage percentage s, the melting point of PET. FTIR results show a change in chemical composition. Particle size, 4-CBA content of MTA affects the properties of the fiber.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".