Experimental methods in chemical engineering: Gas chromatography— <scp>GC</scp>
Bibliographic record
Abstract
Abstract Gas chromatography (GC) separates volatile and semi‐volatile liquids and gases based on their affinity for a stationary phase, either a thin liquid or a polymer layer on a solid. A gaseous mobile phase carries the volatile compounds from an injection port to a column with the stationary phase and then to an amdetector. The most common detectors are based on mass spectrometry (MS), thermal conductivity, and flame ionization. A furnace houses the columns and maintains or ramps temperatures to increase the separation efficiency—reduce run time or increase peak separation (resolution). The analyst chooses the chemistry of the stationary phase and the physical structure of the columns based on the polarity and functional groups of the solute. Packed columns tolerate impurities better than capillary columns that have a greater separating power and bleed the functional groups less. Efficiency is best when the sample polarity matches the column polarity. High temperatures and contamination cause columns to bleed the active components, which reduces the resolution but might also introduce ghost peaks to the chromatogram. The Web of Science assigned 15 000 articles to chemical engineering that mention GC and gas chromatography as keywords. Since 2017, it has indexed close to 1000 new articles every year. A bibliometric analysis classifies these contributions into four clusters: (1) pyrolysis and biomass, (2) GC–MS and extraction, (3) lignin and aromatics, and (4) decomposition and kinetics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.051 |
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".