Determination of the Percentage of Homopolymer Component in Ziegler/Natta Catalyst Linear Low-Density Polyethylene Resins Using High-Temperature Cell Fourier Transform Infrared and Partial Least Squares Quantitative Analysis Technique
Bibliographic record
Abstract
A new method for the determination of the percentage of homopolymer component, using high-temperature cell Fourier transform infrared (FT-IR) by partial least squares (PLS) quantitative analysis technique, was developed and applied to Ziegler Natta linear low-density polyethylene (LLDPE). The method is based on the IR spectrum changes between the 730 cm(-1) band and 720 cm(-1) band at the temperature of 110 degrees C, which is near the melting point of the polyethylene. The HD % (the percentage of high-density component, i.e., the percentage of homopolymer component) results obtained by CTREF (CRYSTAF in TREF mode) technique are used as the input data together with the respective FT-IR spectra for PLS analyses to establish a calibration curve. The PLS quality is characterized by a correlation coefficient of 0.997 (cross-validation) using four factors and a root mean square error of calibration (RMSEC) of 0.772. The HD% of the unknown can then be predicted by the PLS software from the unknown FT-IR spectrum. A control resin was tested seven times by CTREF and FT-IR. The HD% of the control resin was 28.59+/-0.88% by CTREF and 29.05+/-2.37% by FT-IR. It was found that the method was applicable for the same comonomer type of LLDPE within a melt index range and density.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".