Temperature Programming of the Second Dimension in Comprehensive Two-Dimensional Gas Chromatography
Bibliographic record
Abstract
Comprehensive two-dimensional gas chromatography (GC × GC) provides a significant increase in selectivity and peak capacity for the separation of complex mixtures. Optimization of the system is often complicated, with many interconnected parameters between the two dimensions and additional problems like peak wraparound that need to be eliminated or minimized. Wraparound peaks are compounds with retention times in the second dimension that are longer than the modulation period. This results in broad peaks that elute in subsequent modulation cycles, potentially coeluting with separated compounds. The use of a secondary oven is often the solution to the problem. By applying a constant positive temperature offset from the main oven temperature, the retention of all analytes can be reduced so that they elute within their respective modulation periods. However, this reduces the separation of less retained compounds, a classical consequence of the general elution problem due to the isothermal conditions during the limited separation time in the second dimension. To overcome this problem, the second dimension was temperature-programmed by resistively heating an electrically conductive secondary column using constant current. The column was cooled through forced convection inside the GC oven within the time frame of a single modulation period. Temperature programming in the second dimension of GC × GC was able to improve separation while eliminating wraparound peaks and reducing peak widths, leading to significantly increased second dimension peak capacity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".