Fluorous Monolith Specificity: The Effects of Polymer Density and Secondary Interactions on Column Performance and Amenability to Biological Samples
Bibliographic record
Abstract
Continuing from the foundation laid by our previous work in the field, we present here an examination of the effects of monolith density and overall composition on the efficacy of performance in the realm of fluorous separations. By variation of the proportions of monomer and cross-linking agent relative to a static porogenic solvent composition, it was found that a composition of 30% polymer-forming material provides the optimal results in terms of resolution and peak shape for fluorous chromatography of a mixture of similarly labeled benzylamines. The presence of so-called "secondary interactions" that can compete with fluorous specificity in columns of this type were also examined and discussed, with similar results to those observed for commercial fluorous columns being noted. We suggest that these effects may actually be positive if they can be properly harnessed, as the ability to provide a second dimension for fluorous separations based on polarity may allow more complex analyses of labeled proteomic samples to be effectively undertaken. Finally, we present some initial results on the effectiveness of our optimized fluorous monoliths in a series of tagging and separation experiments using a custom-synthesized peptide. With successful resolution of labeled biological samples from their nonfluorous counterparts achieved, we discuss the potential expansion and further applicability of fluorous monoliths of this type in proteomic avenues, as well as their amenability to the greater analytical community.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".