NIST Mass Spectral Libraries in the Context of the Circular Economy of Plastics
Bibliographic record
Abstract
The Mass Spectrometry Data Center (MSDC) has recently started improving existing libraries and creating new ones for identifying and analyzing plastics-related compounds (PRC) and materials (PRM) as part of the NIST circular economy program. PRC are small molecules of dissimilar chemical nature; hence, to increase coverage, we have used three types of ionizations: EI, ESI, and APCI. PRM are solids that include polymers, polymer mixtures, and commercial plastics, so we have used pyrolysis-gas chromatography (py-GC-MS) to create a new searchable library. First, we have increased the coverage of the existing libraries by including as many as possible commercially available PRC. Then, for testing the libraries and to deconvolute complex PRM mixtures, we have analyzed extractable and leachable (E&L) samples and pyrolysis products from one hundred standards of the most common polymers and some of their mixtures using LC-MS/MS, GC-MS, and py-GC-MS. In collaboration with the FDA, the EPA, and other non-government institutions, we are applying techniques, libraries, and tools to areas of interest to the circular economy of plastics, health risk assessments, and environmental challenges.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".