A retrospective look at developments in size exclusion chromatography from the early Hamielec research era to the present day
Bibliographic record
Abstract
Abstract At the start of Archie Hamielec's research career, size exclusion chromatography (SEC) (including gel permeation chromatography [GPC]) was in its infancy as an analytical and preparative laboratory technique in polymer science and engineering. As described below, Archie Hamielec saw its potential early in his academic career and was among the group of pioneers who took it from a developing technique to what it is now. His group's work was instrumental in improving the understanding of the mechanism of separation in SEC and setting up best procedures for sample handling. In addition, much work was done to assess detectors to improve data collection and to obtain consistent/reliable measurements. Importantly, he established it as a key method for obtaining molecular size information to support research in polymer reaction engineering. Nowadays we take it for granted that we can use SEC to obtain molecular weight data rapidly and reliably with user‐friendly computer‐aided analyses. This ease of routine practicality stems from the base that the early researchers built. Although now it is a well‐developed (almost ‘black box’) method there are still areas of novel interest into analysis of complex polymer samples. This has led to modern developments forging ahead in expanding the applicability of SEC as an analytical technique with the exploitation of multi‐detector systems and novel detectors.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".