Effect of Solvent Type on High‐Temperature Thermal Gradient Interaction Chromatography of Polyethylene and Ethylene–1‐Octene Copolymers
Bibliographic record
Abstract
The effects of the solvent type and operation conditions on the high‐temperature thermal gradient interaction chromatography (HT‐TGIC) of ethylene homopolymers, ethylene–1‐octene copolymers, and their blends are investigated. While the HT‐TGIC profiles of single polymers measured with 1,2,4‐trichlorobenzene (TCB) and chloronaphthalene (CN) are similar, they are always narrower when o‐dichlorobenzene (ODCB) is used, particularly for samples with lower 1‐octene fractions. Significant differences between the experimental and the calculated profiles of binary blends are observed with all three solvents, but better peak separation is seen when the ODCB is used. Having higher fractions of a 1‐octene‐poor component in the blend causes a more significant distortion of the shape expected for the peak from the component with the higher 1‐octene fraction. The effect of the molecular weight on HT‐TGIC profiles is also studied using samples with the same comonomer content and different molecular weights. Samples with low molecular weight have broader distributions and significant lower‐temperature tails, particularly when TCB is used. Chain crystallization after adsorption effects may also play a minor role for low‐comonomer samples. Finally, HT‐TGIC profiles are compared with their equivalent crystallization elution fractionation (CEF) profiles. The HT‐TGIC curves are broader than the equivalent CEF profiles, but these differences decrease as the comonomer content increases. image
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".