Microfabrication of a tapered channel for isoelectric focusing with thermally generated pH gradient
Bibliographic record
Abstract
A simple microfabrication technique for the preparation of a tapered microchannel for thermally generated pH gradient isoelectric focusing (IEF) has been demonstrated. The tapered channel was cut into a plastic sheet (thickness was 120 microm), and the channel was closed by sandwiching the plastic sheet between two glass microscope slides. The length of the microchannel was 5 cm. The width of the separation channel was 0.4 mm at the narrow end and 4 mm at the wide end. The channel was coated with polyacrylamide to prevent electroosmotic flow (EOF) during focusing. Two electrolyte vials were mounted on top of each end of the channel with the wide end of the channel connected to the cathodic vial and the narrow to the anodic vial. The feasibility of the thermally generated pH gradient in a tapered channel was demonstrated. Important parameters that determined the feasibility of using a thermally generated pH gradient in a tapered channel were analyzed. Parameters to be optimized were control of EOF and hydrodynamic flow, selection of power supply mode and prevention of local overheating and air bubble formation. Tris-HCl buffer, which has a high pK(a) dependence with temperature, was used both to dissolve proteins and as the electrolyte. The thermally generated pH gradient separation of proteins was tested by focusing dog, cat and human hemoglobins with a whole column detection capillary IEF (CIEF) system.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".