A HoLDI mass spectrometry platform for airborne nanoplastic detection
Bibliographic record
Abstract
There are no established protocols for nanoplastic detection within complex environmental matrices. Mass spectrometry (MS) analysis of environmental nanoplastics is impeded by methodological constraints. We present a versatile platform evolved from matrix-assisted laser desorption/ionization (MALDI) MS for airborne nano/microplastic research. The 3D-printed hollow-laser desorption/ionization (HoLDI) target enables efficient, high-throughput analysis of aerosols collected on simple substrates without pre/post-treatments. HoLDI-MS determines the chemical composition and relative quantity of real-world airborne nano/microplastics, while used with conventional portable samplers and particle analyzers. Polyethylene, polyethylene glycol, and polydimethylsiloxanes are detected in an indoor environment, with a higher amount in the micro-sized range. Polycyclic aromatic hydrocarbons present in an outdoor setting, with a higher quantity in the nano-sized range. Morphological and elemental data provide additional evidence for observed contaminants and support multidisciplinary research interests. HoLDI holds promise as a standardized analytical framework for any air or water samples, facilitating research harmonization worldwide.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".