Targeted Analysis of Microplastics Using Discrete Frequency Infrared Imaging
Bibliographic record
Abstract
An analytical strategy to improve sample throughput with discrete frequency infrared image-based targeted analysis of microplastics using a laser direct infrared chemical imaging system was successfully developed and implemented. Leveraging a quantum cascade laser as a light source, the system could lock the frequency at predetermined wavelengths and use a discrete frequency infrared imaging technique to identify particles with absorption at desired wavelengths. In this way, targeted analysis can be achieved by selectively characterizing these particles. In the concept demonstration study, the targeted analysis was able to identify 87.7% of spiked polyethylene particles by scanning only 20% of the particles in the sample. The technique substantially improves sample throughput by at least a factor of 4 under conditions used. In the tests performed with real environmental samples, the targeted analysis workflow correctly identified eight types of common microplastics by only investigating around 60% of the particles and less than 30% of the sample area. Results obtained demonstrated that this scanning strategy is a game changer to enhance sample throughput in microplastic analysis. The technique has the potential of being applied to other infrared-based analytical platforms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".