Tandem Mass Spectrometry of Alkanolamines in Environmental Samples
Bibliographic record
Abstract
Occurrence and uses of alkanolaminesIn the last 25 years, alkanolamines have been studied extensively because of their industrial importance in natural gas processing plants, synthetic ammonia plants, fossil-fuel-fired power plants, chemical, and petrochemical industries (Mundhwa and Henni, 2007).Alkanolamines are used as chemical intermediates and as surface-active agents in cosmetics, pharmaceuticals, agricultural products, and in various industrial products and applications.Aqueous solutions of alkanolamines are commonly used on a large scale in the natural gas industry to strip and remove acid gases such as carbon dioxide, sulfur dioxide and www.intechopen.comTandem Mass Spectrometry -Applications and Principles 560 hydrogen sulfide) from natural gases and hydrocarbon liquids (Carroll and Marther, 1997; Kohl and Nielsen, 1997 as cited in Hansen et al., 2010).Other uses and applications of alkanolamines are summarized in the Table 1.Application Function Type of alkanolamines Coating Used in both water and solvent based coatings.Increase the solubility of other components and enhance solution stability DEEA and DMEA Emulsifying and Dispersing agents Emulsifying additives in textile, lubricants, polishes, detergents, pesticides and personal care products -hand lotions, shaving creams and shampoos.www.intechopen.com
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".