Influence of ionic constituents and electrical conductivity on the propagation of charged nanoscale objects in passivated gel electrophoresis
Bibliographic record
Abstract
When determining the electric field E acting on charged objects in gel electrophoresis, the electrical conductivity of the buffer solution is often overlooked; E is typically calculated by dividing the applied voltage by a separation distance between electrodes. However, as a consequence of electrolytic reactions, which occur at the electrodes, gradients in the ionic content of the buffer solution and its conductivity can potentially develop over time, thereby impacting E and affecting propagation velocities of charged objects, v, directly. Here, we explore how the types and concentrations of ionic constituents of the buffer solution, which largely control its conductivity, when used in passivated gel electrophoresis (P-gelEP), can influence E, thereby altering v of charged nanospheres propagating through large-pore gels. We measure the conductivity of the buffer solution in the center of the gel region near propagating bands of nanospheres, and we show that predictions of E based on conductivity closely correlate with v. We also explore P-gelEP involving two different types of passivation agents: nonionic polyethylene glycol (PEG) and anionic sodium dodecyl sulfate (SDS). Our observations indicate that using a conductivity model to determine E from the local current density and the conductivity where spheres are propagating can lead to a better estimate than the standard approach of a voltage divided by a separation. Moreover, this conductivity model also provides a starting point for interpreting the complex behavior created by amphiphilic ionic passivation agents, such as SDS, on propagating nanospheres used in some P-gelEP experiments.
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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 | 0.000 | 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) | 0.000 | 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".