Direct Analysis of Plasticizers in Aqueous Samples by Atomspheric Pressure Chemical Ionization-Tandem Mass Spectrometry (APCI-MS-MS
Bibliographic record
Abstract
The widespread manufacture of plastics requires the similarly ubiquitous use of plasticizers. Plasticizers such as bis(2-ethylhexyl) adipate (DOA) and bis(2-ethylhexyl) phthalate (DOP) enhance polymer strength and flexibility and are found in polymeric products such as cosmetics, detergents, and building and storage products (1). However, these additives are not bound to the polymer matrix and are subject to leaching. A recent Health Canada report warned that DOP may leach from medical devices and cause harm to infants, young boys, pregnant women, and nursing mothers (2). The United States Environmental Protection Agency (US. EPA) estimates that over 450,000 pounds of DOA were released to land and water during the period of 1987-1993 (3). Several methods exist for the determination of plasticizers in aqueous samples. For example, U.S. EPA methods 506 and 525.1 ma)) be used to analyze drinking water for DOA and DOE among other organic compounds (4,5). Extraction of the analytes from the water matrix is achieved by either liquid-liquid extraction or by passing the sample through a solid-phase extraction disk. Extracts are analyzed by gas chromatography (GC) with either photoionization (method 506) or mass spectrometric detection (MS) (method 525.1). Recently, a liquid chromatography-mass spectrometry (LC-MS) method for the analysis of plasticizers in water was reported (6). Regardless of the instrumental method employed, all of these methods require sample volumes ranging from 200 to 1000 mL in addition to lengthy liquid-liquid or solid-phase extraction procedures. Furthermore, both soluble and immiscible analytes are partitioned into the organic phase and quantitated as though the entire quantity were completely soluble in the sample. Not only does the extractionless method reported here reduce the sample volume required for analysis, but it also drastically reduces the labor required to prepare the samples. .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".