Synthesis of Novel Size Exclusion Chromatography Support by Surface Initiated Aqueous Atom Transfer Radical Polymerization
Bibliographic record
Abstract
We report the use of aqueous surface-initiated atom transfer radical polymerization (SI-ATRP) to grow polymer brushes from a "gigaporous" polymeric chromatography support for use as a novel size exclusion chromatography medium. Poly(N,N-dimethylacrylamide) (PDMA) was grown from hydrolyzable surface initiators via SI-ATRP catalyzed by 1,1,4,7,10,10-hexamethyltriethylenetetramine (HMTETA)/CuCl. Grafted polymer was characterized semiquantitatively by ATR-FTIR and also cleaved and quantitatively characterized for mass, molecular weight, and polydispersity via analytical SEC/MALLS. The synthesis provides control over graft density and allows the creation of dense brushes. Incorporation of negative surface charge was found to be crucial for improving the initiation efficiency. As polymer molecular weight and density could be controlled through reaction conditions, the resulting low-polydispersity grafted polymer brush medium is shown to be suitable for use as a customizable size exclusion chromatography medium for investigating the principals of entropic interaction chromatography. All packed media investigated showed size-dependent partitioning of solutes, even for low graft density systems. Increasing the molecular weight of the grafts allowed solutes more access to the volume fraction in the column available for partitioning. Compared to low graft density media, increased graft density caused eluted solute probes to be retained less within the column and allowed for greater size discrimination of probes whose molecular weights were less than 10(4) kDa.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".