<tt>LitChemPlast</tt>: An Open Database of Chemicals Measured in Plastics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Plastics contain various chemical substances, which can impact human and ecosystem health and the transition to a circular economy. Meanwhile, information on the presence of individual substances in plastics is generally not made publicly available, but relies on extensive analytical efforts. Here, we review measurement studies of chemicals in plastics and compile them into a new LitChemPlast database. Over 3500 substances, stemming from all plastic life-cycle stages, have been detected in different plastics in 372 studies. Approximately 75% of them have only been detected in nontargeted workflows, while targeted analyses have focused on limited well-known substances, particularly metal(loid)s, brominated flame retardants, and ortho -phthalates. Some product categories have rarely been studied despite economic importance, e.g., consumer and industrial packaging (other than food packaging), building and construction, and automotive plastics. Likewise, limited studies have investigated recycled plastics, while existing measurements of recycled plastics show higher detection frequencies and median concentrations of regulated brominated flame retardants across many product categories. The LitChemPlast database may be further developed or utilized, e.g., for exposure assessment or substance flow analysis. Nonetheless, the plethora of relevant substances and products underscores the necessity for additional measures to enable the transition to a safe circular plastics economy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.062 |
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".