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. Detecting nanoplastics in complex environmental samples is hampered by the lack of established protocols and methodological and technical constraints. Here, the authors show that matrix-assisted laser desorption/ionization (MALDI) mass spectrometry with a 3D-printed hollow-laser desorption/ionization (HoLDI) target enables efficient analysis of aerosols, providing size-stratified information on chemical composition and relative quantities of airborne nano- and microplastics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".