Use of FT‐IR Spectrometry for On‐Line Detection in Temperature Rising Elution Fractionation
Bibliographic record
Abstract
Abstract Summary: Temperature rising elution fractionation (TREF) has become a popular analytical technique that is able to determine the chemical composition distribution (CCD) of an ethylene/α‐olefin copolymer. An infrared (IR) detector is commonly used in TREF detection to measure the concentration of the polymer solution exiting the column as a function of elution temperature. The chemical composition of the eluting polymer at a given elution temperature can be predicted from the relationship between comonomer content and TREF elution temperature pre‐established through 13C nuclear magnetic resonance (NMR) analysis of TREF fractions. In this article, a Fourier transform infrared (FT‐IR) spectrometer has been coupled with a TREF instrument to provide a more powerful tool for characterizing complex olefin copolymers. The Partial Least Squares (PLS) technique is used when analyzing the FT‐IR spectra of the eluting polymer solutions. The power of on‐line FT‐IR detection in TREF is demonstrated using a few complex copolymer systems, such as ethylene‐octene copolymer, polystyrene grafted ethylene‐vinyl acetate copolymer and ethylene‐methyl acrylate copolymer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".