Data-Driven Collision Cross Section Library for Structural Analysis and Identification of Expanded Polystyrene Buoy Photodegradation Products in Aquaculture
Bibliographic record
Abstract
The environmental impact of photodegradation products from Expanded Polystyrene (EPS) aquaculture buoys poses a growing ecological concern. Structural characterization of these degradation products, particularly isomeric intermediates, has been limited. This study addresses these challenges by developing a compound-specific collision cross-section (CCS) database using cyclic ion mobility-mass spectrometry (cIM-MS) combined with in silico CCS calculations. A total of 49 photodegradation products were identified. Calculated CCS values showed discrepancies of less than 5% when compared with experimental measurements and were further validated using six reference standards. Despite improvements in resolution through multipass cIM-MS experiments, isomers with CCS differences less than 1 Å 2 remained unresolved. To overcome this, Gibbs free energy calculations were conducted, providing additional identification confidence in distinguishing structurally similar isomers. The resulting CCS database enhances the accuracy of compound identification in environmental monitoring and supports ecological risk assessments associated with EPS degradation. This work highlights the value of integrating experimental ion mobility data with computational modeling to resolve structural ambiguities in complex environmental mixtures.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".