Microplastic Mass Quantification Using Focal Plane Array-Based Micro-Fourier-Transform Infrared Imaging
Bibliographic record
Abstract
The quantification of microplastics (MPs) in environmental samples on a mass basis can be used to provide a more comprehensive understanding of the fate and transport of MPs in the environment. In this study, a precise method for quantifying the volumes and, by extension, the masses of MPs was developed. This novel approach is grounded in the principles of Beer’s law and makes use of focal plane array (FPA) micro-Fourier-transform infrared imaging. This methodology capitalizes on the absorption characteristics observed at each pixel within FPA imaging data, to identify variations in thickness across the x–y plane and facilitate a comprehensive characterization of the 3D geometries of MPs. This approach represents an advancement from previous assumptions that treated all MPs as regularly shaped and extrapolated thickness information solely based on x–y coordinates. Linear regression was used to model the relationship between absorbance and plastic thickness, drawing from data collected from plastic membranes with controlled thickness. The model was validated through a comparison between known and estimated volumes of MPs characterized by well-defined geometries, yielding errors <5%, substantiating the validity and accuracy of the proposed approach. The proposed method developed in this study holds the potential to emerge as a standard protocol for the accurate quantification of MP mass in environmental samples.
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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 | 0.001 |
| 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".