Analysis of Monoethanolamine and Its Oxidative Degradation Products during CO<sub>2</sub> Absorption from Flue Gases: A Comparative Study of GC-MS, HPLC-RID, and CE-DAD Analytical Techniques and Possible Optimum Combinations
Bibliographic record
Abstract
A comparative study of gas chromatography−mass spectrometry (GC-MS), high-performance liquid chromatography−refractive index detection (HPLC-RID), and capillary electrophoresis−diode array detection (CE-DAD) techniques was carried out for the purpose of analyzing MEA and its degradation products in MEA/H 2 O/O 2 and MEA/H 2 O/O 2 /CO 2 systems. The experiments were conducted in a 600-mL reactor using an MEA concentration of 5 kmol/m 3, an O 2 pressure of 250 kPa, a CO 2 loading of 0.51 mol of CO 2 /mol of MEA, and degradation temperatures of 328−393 K. GC-MS using an HP-35MS column (intermediate polarity) performed the best only if analysis of the degradation products was of interest, whereas HP-Innowax (high-polarity column) was best only if analysis of MEA was required. Analyses of the same sample using two different columns (e.g., HP-35MS and HP-Innowax) would be required if both MEA and its degradation products are to be followed. HPLC-RID using a Nucleosil column with phosphate buffer was the best and only technique in which simultaneous analysis of MEA and degradation products was possible. CE-DAD using phosphate and borate electrolytes was able to detect degradation products. Because the results in terms of degradation product distribution, decline of MEA, and role played by CO 2 as observed by all techniques were consistent, a combination of these techniques is recommended for confirming MEA oxidative degradation systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".