Simplified universal method for determining electrolyte temperatures in a capillary electrophoresis instrument with forced‐air cooling
Bibliographic record
Abstract
Temperature increase due to resistive electrical heating is an inherent limitation of capillary electrophoresis (CE). Active cooling systems are used to decrease the temperature of the capillary, but their capacity is limited; and in addition, they leave "hot spots" at the detection interface and at the capillary ends. Until recently, the matter was complicated by the lack of a fast and generic method for temperature determination in efficiently and inefficiently cooled regions of the capillary. Our group recently introduced such a method, termed "Universal Method for determining Electrolyte Temperatures" (UMET). UMET is a probe-less approach that requires only measuring current versus voltage for different voltages and processing the data using an iterative algorithm. Here, we apply UMET to develop a Simplified Universal Method of Temperature Determination (SUMET) for a CE instrument with a forced-air cooling system using an Agilent 7100 CE instrument (Agilent Technologies, Saint Laurent, Quebec, Canada) as an example. We collected a wide set of empirical voltage-current data for a variety of buffers and capillary diameters. We further constructed empirical equations for temperature calculation in efficiently and inefficiently cooled parts of the capillary that require only the data from a single 1-min voltage-current measurement. The equations are specific for the Agilent 7100 CE instrument (Agilent Technologies) but can be applied to all kinds of capillaries and buffers. Similar SUMET approaches can be developed for other CE instruments with forced-air cooling using our approach.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".