LC-ESI-MS-MS method for the analysis of tetrabromobisphenol A in sediment and sewage sludge
Bibliographic record
Abstract
Tetrabromobisphenol A (4,4'-isopropylidenebis(2,6-dibromophenol), TBBPA) is the most widely used brominated flame retardant in the world. Due to its low water solubility TBBPA released in aquatic ecosystems ultimately accumulates in sediments, but the lack of data on its environmental level and temporal trend in sediment cores precludes establishing if the input of TBBPA is an on-going environmental problem. We developed an analytical method involving HPLC-ESI-MS-MS (ion trap) with detection of the negative pseudo-molecular ion of TBBPA and its fragmentation pattern. Recovery of TBBPA from spiked marine sediment (both lyophilized and wet) and dehydrated sewage sludge was better than 95%. The current detection limit of TBBPA is 60 pg injected and the linearity of the response is at least three orders of magnitude, ranging from 7 ng ml(-1) to 7000 ng ml(-1). The method was also applied to the analysis of urban sewage sludge where TBBPA was detected at a concentration of 300 ng g(-1)(dry weight). With an analysis time of less than 20 min, this method is adequate for a rapid re-assessment of archived sediment samples avoiding cumbersome derivatization procedures.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".