The molecular end effect and its critical impact on the behavior of charged‐uncharged polymer conjugates during free‐solution electrophoresis
Bibliographic record
Abstract
Recently two novel techniques using free-solution electrophoresis to separate charged-uncharged polymer conjugates have proven successful: end-labeled free-solution electrophoresis (ELFSE) for DNA sequencing, and free-solution conjugate electrophoresis (FSCE) for molar mass profiling of uncharged polymers. The approach taken to analyze the experimental data was an extension of the theory of Long and co-workers (Long, D., Dobrynin, A. V., Rubinstein, M., Ajdari, A., J. Chem. Phys. 1998, 108, 1234-1244) for the electrophoresis of molecules with varying charge distributions. This theory also predicts that the ends of the polymers play a large role in determining the polymer's overall mobility; however, this aspect of the theory was neglected in previous work. Until now this "end effect" has, to the knowledge of the authors, not been recognized in experimental data. Through a careful investigation of the predicted end effect and a reanalysis of the experimental data, we demonstrate that indeed this effect critically impacts on the behavior of charged-uncharged polymer conjugates during electrophoresis. This work indicates that not only does the end effect need to be taken into account to avoid significant errors in data analysis, but also it provides novel system optimization approaches.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".