Joint Effect of Poly(ethyhlene‐<i>co</i>‐1‐octene) Chain Length and 1‐Octene Fraction on High‐Temperature Thermal Gradient Interaction Chromatography
Bibliographic record
Abstract
High‐temperature thermal gradient interaction chromatography (HT‐TGIC) fractionates polyolefins based on an adsorption–desorption mechanism. Several factors influence the shape and position of HT‐TGIC chromatograms, notably polymer microstructure, analytical conditions, and, to a lesser extent, solvent type. This article investigates the joint influence of chain length and comonomer content of a series of polyethylene and ethylene/1‐octene copolymers having similar 1‐octene fractions (0–13 mol%) and a wide range of molecular weights on HT‐TGIC fractionation. For each series of copolymers having similar 1‐octene fraction, the elution peak temperature decreases exponentially and the profiles become increasingly broader below a critical number average chain length value. The authors use Monte Carlo simulation and Stockmayer distribution to explain the observed behavior, finding that no simple correlation exists between ethylene sequences in the copolymers and peak elution temperature, but that there is strong evidence that axial dispersion is responsible for symmetrical broadening of the HT‐TGIC profiles. The authors also study the HT‐TGIC of binary blends, finding that components with similar 1‐octene contents and dissimilar chain lengths tend to increase co‐adsorption/co‐desorption effects. image
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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) | 0.001 | 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".