Low Thermal Mass Liquid Chromatography
Bibliographic record
Abstract
A novel technique, low thermal mass liquid chromatography (LTMLC), is introduced in this study. The use of an LTM assembly that utilizes the principle of resistive wire heating and a temperature sensor to accurately deliver unprecedented heating (up to 1800 degrees C/min) or cooling (100 to approximately 200 degrees C/min) rates is reported. With the use of packed microcolumns (<0.5 mm i.d.), essentially instantaneous heat transfer from the assembly to the mobile phase was obtained. A systematic investigation was conducted to study the performance of the LTMLC technique. Both isocratic and gradient mobile phase conditions were used. For temperature control, isothermal, temperature-increasing, and temperature-decreasing gradients were applied. Three model mixtures, two of which containing neutral and acidic analytes and the other containing neutral, acidic, and basic analytes, were used to study the effect of temperature on elution time, resolution, column efficiency, and selectivity. It was found that the LTMLC experimental setup delivered reliable temperature control, as evidenced by linear van't Hoff plots for neutral and acidic compounds. The effect of temperature on the elution of basic analytes yielded nonlinear van't Hoff plots, explaining the dramatic selectivity changes observed for bases with changes in column temperature. Column efficiency generally increased with the increase in column temperature in the range of 25 to approximately 75 degrees C and decreased in the range of 75 to approximately 150 degrees C at a fixed column flow rate (3 microL/min), when extra column band broadening was taken into account. The increase in efficiency upon the increase in column temperature in the low temperature range was mainly due to the decreased mass transfer term resulting from increased analyte diffusivity. However, under even higher temperatures, the longitudinal diffusion dominated band broadening, explaining the decrease in column efficiency upon a further increase in column temperature. Resolution and selectivity decreased at elevated temperature for neutral and acidic compounds. For mixtures that contain bases, improved resolution was obtained by simultaneously tuning temperature and solvent programming. In addition to heating ability, LTMLC also demonstrated reliable cooling capability, allowing performance of oscillated or cycled temperature programming for fine-tuning the separation of critical band pairs for the first time. Finally, ultrafast reproducible LTMLC was also demonstrated, showing the potential of utilization of this technology for fast and ultrafast separations.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".