A comparison of hydrocarbon and alkali metal response in the flame ionization detector used in subcritical water chromatography
Bibliographic record
Abstract
The flame ionization detector (FID) response toward alkali metals and hydrocarbons was compared. Optimal hydrogen flame gas flow rates were found near 40 mL/min for hydrocarbon response and 80 mL/min for alkali response. While each displayed a linear FID response, alkali metals produced several orders of magnitude greater detector sensitivity than hydrocarbons. Of note, KCl, NaCl, LiCl, and ethanol yielded respective FID sensitivity of about 7500, 980, 130, and 1 mV/μg analyte. This was subsequently demonstrated to greatly alter the FID response of organic salts. For example, while formic acid is normally unresponsive in an FID, its potassium salt could be readily detected here at picogram levels. Conversely, this phenomenon also rendered the FID unsuitable for use with buffered mobile phases containing such salts. In particular, FID background and baseline noise levels for formic acid – sodium formate buffers were about 10 times larger than equivalent experiments with methanol–water and up to two orders of magnitude larger than pure water. Overall, the results show that alkali metals respond much stronger in the FID than do hydrocarbons. Accordingly, their presence in organic analytes or mobile phases must therefore be accounted for when using this detector, particularly in areas such as subcritical water chromatography where it is commonly employed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".