Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection
Bibliographic record
Abstract
For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that automatically classifies nano- and microplastics simultaneously from nonplastic particles within milliseconds in stationary and dynamic natural waters, without sample preparation. AI-assisted nano-DIHM identifies 2 and 1% of waterborne particles as nano/microplastics in Lake Ontario and the Saint Lawrence River, respectively. Nano-DIHM provides physicochemical properties of single particles or clusters of nano/microplastics, including size, shape, optical phase, perimeter, surface area, roughness, and edge gradient. It distinguishes nano/microplastics from mixtures of organics, inorganics, biological particles, and coated heterogeneous clusters. This technology allows 4D tracking and 3D structural and spatial study of waterborne nano/microplastics. Independent transmission electron microscopy, mass spectrometry, and nanoparticle tracking analysis validates nano-DIHM data. Complementary modeling demonstrates nano- and microplastics have significantly distinct distribution patterns in water, which affect their transport and fate, rendering nano-DIHM a powerful tool for accurate nano/microplastic life-cycle analysis and hotspot remediation.
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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 | 0.001 | 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 | 0.000 |
| 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".