Development of a sensitive method for the quantitative analysis of 34 organophosphate esters in seabird eggs, liver, and fish liver tissue
Bibliographic record
Abstract
Organophosphate esters (OPEs) are a diverse group of synthetic organic chemicals used in a variety of plastics as plasticizers, flame retardants, and other application. OPEs were initially considered to pose minimal environmental risk. However, there has been increasing evidence that OPEs can undergo long-range transport into remote areas such as the Canadian Arctic via air, water, or associated to plastic particles. Moreover, Arctic seabirds with high loads of ingested plastics have been found to have elevated concentrations of OPEs, suggesting that plastics containing OPEs might act as sources of OPEs in biota. Further research into OPE mixtures that biota are exposed to is warranted. Yet, existing methods for OPE analysis in biota typically cover fewer than 20 OPEs and few tissue types. Here we present a method for the analysis of 34 OPEs in seabird eggs and liver tissue, as well as fish liver tissue using ultrasound assistant extraction, solid-phase extraction clean-up, and high-performance liquid chromatography coupled with atmospheric pressure chemical ionization high-resolution mass-spectrometry. The validated method showed good linearity (R 2 > 0.99), average recoveries for native and isotope-labelled analytes between 70% and 120%, and inter-day precision of between 2.25% and 25.4% (median <15%). The variety of OPEs included in this new method enables the investigation of OPEs with a broad range of physical-chemical properties and applications in biota samples. The detection of highly non-polar OPEs in Arctic biota tissue highlights the need for further investigation of the bioaccumulation potential of these substances. Moreover, the high detected concentrations of up to 147 ng g −1 ww of the OPE metabolite Bis(1,3-dichloro-2-propyl) phosphate (BDCP) in livers of Arctic seabirds show that these animals were exposed to chlorinated OPEs even if they were not detectable in the tissue.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".