Label-Free Identification and Imaging of Microplastic and Nanoplastic Biouptake Using Optical Photothermal Infrared Microspectroscopy
Bibliographic record
Abstract
As plastic waste breaks down into smaller fragments in the environment, it poses a significant threat to both terrestrial and aquatic ecosystems as well as exposed humans via contaminated water, air, and food. There is thus a critical need to understand the biological uptake and subsequent impacts of plastic particles in aquatic and terrestrial organisms. Yet, we lack effective and robust methodologies to identify and localize micrometer and nanometer-sized polymer particles in whole organisms. This proof-of-concept study introduces a label-free approach for the localization and identification of plastic particles within organisms utilizing optical photothermal infrared microscopy (O-PTIR) and microtome techniques. By integrating O-PTIR imaging with microtomy, we achieved high spatial resolution and sensitivity, allowing us to detect and identify different plastic particles (polystyrene, polyethylene, polypropylene, and poly(methyl methacrylate)) and confirm their localization in a tissue sample. The results demonstrate successful visualization of microplastics and nanoplastics at moderate exposure concentrations in a range of aquatic and terrestrial organisms; namely, Daphnia magna, Drosophila melanogaster, and Eisenia andrei . By eliminating the need for labeling and offering submicron resolution, this vibrational microspectroscopy-based approach emerges as a promising tool for advancing our understanding of the distribution and potential impacts of microplastics and nanoplastics.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".