Determination of Phthalate Ester Congeners and Mixtures by LC/ESI-MS in Sediments and Biota of an Urbanized Marine Inlet
Bibliographic record
Abstract
Phthalate esters (PEs) are a group of widely used commercial chemicals consisting of many different congeners. Concentrations of di(2-ethylhexyl) phthalate ester in the parts per million range have been observed in sediments from locations in North America and Europe. However, sediment and biota concentrations of other widely used PEs (i.e., dibutyl phthalate, diisononyl phthalate, and diisodecyl phthalate) are rare and often in doubt because of analytical difficulties. One of the problems is that commercial formulations predominantly consist of PEs with a specific molecular weight but include many isomers within each molecular weight class. Currently there are no analytical methods or required standards to fully separate PEs into the different molecular weight classes corresponding to the formulations from which they originate. Hence, ambient total and mixture-specific PE concentrations do not exist. This study presents a new method based on reversed-phase liquid chromatography/ electrospray ionization mass spectrometry (LC/ESI-MS) for the quantitative determination of individual PEs, including six congeners on the U.S. EPA Priority pollutant list and several commercial PE isomeric mixtures, in complex environmental matrixes. The method is applied to determine the composition of PE concentrations in sediments and fish in an urbanized marine ecosystem. PE fingerprints in sediments show a predominance of high molecular weight PEs and match per capita consumption levels of PEs. Fingerprints in fish tissue show a predominance of low molecular weight PEs and do not match per capita consumption levels. The findings indicate that the higher molecular weight PEs are less biologically available than the lower molecular weight ones.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".