Abiotic degradations of legacy and novel flame retardants in environmental and food matrices – a review
Bibliographic record
Abstract
Flame retardants (FRs) are commonly added to commercial products to achieve flammability resistance. Since most of them are not chemically bonded to the materials, they could be leached to the environment during the production and disposal cycle. These FRs were categorised based on their chemical nature, including brominated, organophosphorus-, mineral- and nitrogen-based. This review summarised the abiotic degradation reactions of these four classes of FRs, with a focus on thermal and photodegradation reactions in environmental and food matrices. Only 24 papers have reported related information on abiotic degradation reactions that could be useful for predicting possible degradation pathways, and most focused on brominated FRs. Most studies also investigated the thermal degradation of FRs under high temperatures (>400 °C), which exceeds the normal cooking temperature at 100-300 °C. For photodegradation, studies have used up to five times the energy typically used in UV radiation during food processing. It is recommended that future studies investigate the fate of these FRs in foods under more realistic processing conditions, to provide a more comprehensive picture of the estimated consumption of FRs and their degradation products from foods, and facilitate a better risk assessment of the use of these novel FRs.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".