Diffusion as a Tool of Measuring Temperature inside a Capillary
Bibliographic record
Abstract
Application of capillary electrophoresis (CE) to temperature-sensitive biomolecular interactions requires knowledge of the temperature inside the capillary. The simplest approach to finding temperature in CE employs a molecular probe with a temperature-dependent parameter. Up until now only spectral parameters of molecular probes were utilized for temperature measurements in CE. The arbitrary nature of spectral parameters leads to several inherent limitations that compromise the accuracy and precision of temperature determination. This paper introduces the concept of finding temperature in CE through the measurement of a nonspectral parameter of the molecular probeits diffusion coefficient. Diffusion is a fundamental property of molecules that depends only on the molecular structure of the probe, the nature of the environment, and the temperature. It is ideally suited for temperature measurements in CE if an approach for measuring the diffusion coefficient in a capillary with high precision is available. This work first develops an approach for measuring the diffusion coefficient in a capillary with a relative standard deviation of as low as 2.1%. It is then demonstrated that such precise measurements of the diffusion coefficient could facilitate accurate temperature determination in CE with a precision of 1 degrees C. This new method was used to study the effect on temperature of different amounts of joule heat generated and different efficiencies of heat dissipation. The nonspectroscopic nature of the method makes it potentially applicable to nonspectroscopic detection schemes, for example, electrochemical and mass spectrometric detection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".