Temperature Programming of the Second Dimension in Comprehensive Two-Dimensional Gas Chromatography (GC×GC)
Bibliographic record
Abstract
Comprehensive two-dimensional gas chromatography (GC×GC) is a mature separation technique that is now over three decades old. It separates all sample components using two GC columns with different selectivities, connected in series through a special interface called a modulator to provide two dimensions of separation. Early research and development of this technique focused on the modulator, the most critical component in a GC×GC system. The modulator facilitates the collection and transfer of effluent from the first dimension (¹D) to the second dimension (²D) column. As the modulator development matured with various designs being commercialized, research focus shifted towards chemometrics and applications. However, a fundamental issue that has not been resolved thus far in GC×GC is the general elution problem in ²D due to the nearly isothermal conditions in this dimension during a single separation. This thesis details the development, testing, and application of a ²D temperature programming system for GC×GC to overcome this problem. \n \nEvery stage of development for the ²D temperature programming system is described, from the proof-of-concept to the final design. The proof-of-concept system in Chapter 2 focused on the heating and cooling aspects of ²D temperature programming. It featured a ballistic heating system that synchronized the heating and cooling of the ²D column to the modulation period. Although the final temperature offset could be pre-calibrated, the system did not feature any real temperature control. Despite its simplicity, ballistic heating eliminated wraparound peaks while maintaining the separation of weakly retained compounds in the ²D. \n \nThe first prototype of a true ²D temperature programming system, detailed in Chapter 3, featured an Arduino microcontroller for PID temperature control. The temperature difference between the ²D column and the GC oven was measured directly using two thermocouples connected differentially. Although ²D temperature programming with the prototype improved the peak capacity by 48 %, the fragility of the thermocouple attachment was an issue for commercial designs. \n \nThe final version of the ²D temperature programming system (²DTPS), described in Chapter 4, tackled the robustness and user-friendliness of the system, building upon the core functions from the prior designs. The temperature measurement system was completely redesigned to determine the ²D column temperature based on the ²D column electrical resistance. The column connections and column cage were also redesigned to incorporate longer ²D columns and provide a more secure connection to the column for heating and resistance measurement. A real time clock and remote port were also added to allow the ²DTPS to be a completely standalone device for any GC×GC system. A Windows Forms application was written to control and monitor the ²DTPS. System compatibility was tested with both thermal and flow modulators, as well as a flame ionization detector (FID) and time-of-flight mass spectrometer (TOFMS). \n \nFinally, the ²DTPS was applied to the characterization of renewable hydrocarbon samples from a local Ontario company in Chapter 5. Samples from various points in the production process were characterized to help with product development. The GC×GC separation with the ²DTPS was optimized with both a normal and a reverse column set, as well as two detectors, an FID and a TOFMS. The ²DTPS improved the signal-to-noise ratio and peak capacity for the characterization of the samples.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.002 |
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".