Using Nonequilibrium Capillary Electrophoresis of Equilibrium Mixtures for the Determination of Temperature in Capillary Electrophoresis
Bibliographic record
Abstract
Until now, all methods for temperature sensing in capillary electrophoresis (CE) relied on molecular probes with temperature-dependent spectral/optical properties. Here we introduce a nonspectroscopic approach to determining temperature in CE. It is based on measuring a temperature-dependent rate constant of complex dissociation by means of a kinetic CE method known as nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM). Conceptually, a calibration curve of "the rate constant versus temperature" is built using NECEEM and a CE instrument with a reliable temperature control or, alternatively, a nonelectrophoretic method, such as surface plasmon resonance. The calibration curve is then used to find the temperature during CE in the same buffer but with another CE apparatus or under otherwise different conditions (cooling efficiency, length and diameter of the capillary, electrical field, etc.). In this proof-of-principle work, we used the dissociation of a protein-DNA complex to demonstrate that the NECEEM-based temperature determination method allows for temperature determination in CE with a precision of 2 degrees C. Then, we applied the NECEEM-based temperature determination method to study heat dissipation efficiency in CE instruments with active and passive cooling of the capillary. The nonspectroscopic nature of the method makes it potentially applicable to nonspectroscopic detection schemes, e.g. electrochemical detection. A "kinetic probe" can be coloaded into the capillary along with a sample for in situ temperature measurements. Higher order chemical reactions can also be used for temperature sensing, provided a kinetic CE method for measuring a corresponding rate constant is available.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".