Quantitative determination of short‐chain branching content and distribution in commercial polyethylenes by thermally fractionated differential scanning calorimetry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A method for rapid quantitative analysis of the content and distribution of short chain branching (SCB) for α‐olefin/ethylene copolymers based on thermally fractionated DSC is presented. Eight commercial polyethylenes, four made with conventional Ziegler‐Natta catalysts and four made with metallocene catalysts, were analyzed by differential scanning calorimetry (DSC), after having been thermally segregated by successive nucleation annealing (SNA). The polyethylenes were also analyzed by temperature rising elution fractionation (TREF) and carbon‐13 nuclear magnetic resonance ( 13 C‐NMR). The SNA‐DSC procedure segregates polyethylenes according to methylene sequence lengths (MSL). The relationship between DSC melting temperature and SCB content was obtained by calibration with linear hydrocarbons; TREF results were not used in the SNA‐DSC calibration. Deconvolution of the SNA‐DSC endotherms yielded estimates of the average SCB contents and SCB distributions. The SCB contents obtained from the SNA‐DSC for linear low density polyethylenes agreed very well with the SCB contents obtained by 13 C‐NMR and TREF, and the SCB distributions measured by SNA‐DSC were very similar to those obtained by TREF. The SCB contents obtained by SNA‐DSC for ultra‐low density polyethylenes, made with metallocene catalysts, were about 20% lower than the values obtained by 13 C‐NMR; the values obtained by TREF were even lower.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it