Identification and Quantitation of Sorbitol-Based Nuclear Clarifying Agents Extracted from Common Laboratory and Consumer Plasticware Made of Polypropylene
Bibliographic record
Abstract
Reported here is the mass spectral identification of sorbitol-based nuclear clarifying agents (NCAs) and the quantitative description of their extractability from common laboratory and household plasticware made of polypropylene. NCAs are frequently added to polypropylene to improve optical clarity, increase performance properties, and aid in the manufacturing process of this plastic. NCA addition makes polypropylene plasticware more aesthetically pleasing to the user and makes the product competitive with other plastic formulations. We show here that several NCAs are readily extracted with either ethanol or water from plastic labware during typical laboratory procedures. Observed levels ranged from a nanogram to micrograms of NCA. NCAs were also detected in extracts from plastic food storage containers; levels ranged from 1 to 10 microg in two of the three brands tested. The electron ionization mass spectra for three sorbitol-based nuclear clarifying agents (1,3:2,4-bis-O-(benzylidene)sorbitol, 1,3:2,4-bis-O-(p-methylbenzylidene)sorbitol, 1,3:2,4-bis-O-(3,4-dimethylbenzylidene)sorbitol) are presented for the native and trimethylsilyl-derivatized compounds together with the collision-induced dissociation mass spectra; gas and liquid chromatographic data are also reported. These NCAs now join other well-known plasticizers such as phthalate esters and bisphenol A as common laboratory contaminants. While the potential toxicity of NCAs in mammalian systems is unknown, the current data provide scientists and consumers the opportunity to make more informed decisions regarding the use of polypropylene plastics.
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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) | 0.000 | 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) | 0.001 | 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".