Bibliographic record
Abstract
Abstract This article highlights techniques by which the enantiomers of chiral environmental pollutants can be separated and quantified. A large number of organic chemicals are chiral and exist as pairs of mirror images calledenantiomers. These chemicals include legacy persistent organic pollutants (POPs) (e.g. polychlorinated biphenyls (PCBs)) and pesticides (e.g. dichlorodiphenyltrichloroethane (DDT)), as well as current‐use pesticides (e.g. pyrethroids), flame retardants (e.g. hexabromocyclododecane), and pharmaceuticals (e.g. ibuprofen). Understanding the environmental behavior of chiral xenobiotic compounds is important because enantiomers of a chiral compound may have different biological and toxicological effects, which must be delineated for accurate risk assessment of hazards, if any, posed by such chemicals. In addition, chiral chemicals are markers of biochemical activity in the environment, as enantiomer compositions are unaffected by physical and chemical processes but can change from differential enantiomer interactions with other chiral molecules (e.g. enzymes). For these reasons, the chirality of environmental pollutant occurrence, fate, and effects has been studied since the 1990s, when analytical capacity to measure chiral chemicals became widely available. We discuss major techniques for separating pollutant enantiomers including gas chromatography (GC), high‐performance liquid chromatography (HPLC), and capillary electrophoresis (CE). Enantioselective analytical separations are often coupled to mass spectrometry (MS) and tandem mass spectrometry (MS/MS) for quantification under environmentally relevant conditions (i.e. low concentrations to parts per trillion and below, in highly complex matrices such as wastewater and biological tissues). Issues involving sample preparation, data handling, and quality assurance/quality control are also described. Finally, we also illustrate applications of enantiomer‐specific measurements to gain insights into pollutants affecting environmental processes that could otherwise not be obtained, such as assessing pollutant biodegradation and delineating pollutant sources.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".