Bibliographic record
Abstract
Abstract Polymer fractionation techniques attempt to fractionate polymers according to specific characteristics of their microstructures as defined by their distributions of molecular weight, chemical composition, comonomer sequence length, tacticity, and long‐chain branching. Because of the heterogeneous nature of polymers, fractionation techniques are essential for understanding structure‐property relationships, polymerization mechanisms and kinetics, and polymer reaction engineering. In this entry, theoretical models to describe the microstructure of polymers are initially reviewed and used as a basis to understand the several polymer fractionation techniques described in the subsequent sections. Batch fractionation methods are covered next, including temperature variation and solvent/nonsolvent fractionation procedures. The recent technique of crystallization analysis fractionation (Crystaf), together with temperature rising elution fractionation (TREF), are reviewed as methods for fractionation by crystallizability. The well‐established technique for molecular weight fractionation by size‐exclusion chromatography (SEC) is covered next, followed by a section on the versatile field flow fractionation (FFF). Large‐scale fractionation of polymers is covered in the section for continuous polymer fractionation (CPF). Mass spectrometry, particularly matrix‐assisted laser desorption ionization coupled with time‐of‐flight analyzers (MALDI‐TOF) is the subject of the next section. Finally, a section in interaction chromatography and some other less general polymer fractionation techniques is followed by the concluding section on cross‐fractionation techniques.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.114 | 0.159 |
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".