3D imaging photocatalytically degraded micro- and nanoplastics
Bibliographic record
Abstract
Abstract Microplastics (MPs) and nanoplastics have been an emerging global concern, with hazardous effects on plant, animal, and human health. Their small size makes it easier for them to spread to various ecosystems and enter the food chain; they are already widely found in aqueous environments and within aquatic life, and have even been found within humans. Much research has gone into understanding micro-/nanoplastic sources and environmental fate, but less work has been done to understand their degradation. Photocatalytic degradation is a promising green technique that uses visible or ultraviolet light in combination with photocatalyst to degrade plastic particles. While complete degradation, reducing plastics to small molecules, is often the goal, partial degradation is more common. We examined microscale polyethylene (PE) (125–150 µ m in diameter) and nanoscale polystyrene (PS) (∼300 nm in diameter) spheres both before and after degradation using multiple imaging techniques, especially electron tomography in addition to conventional electron microscopy. Electron tomography is able to image the 3D exterior and interior of the nanoplastics, enabling us to observe within aggregates and inside degraded spheres, where we found potentially open interior structures after degradation. These structures may result from differences in degradation and aggregation behavior between the different plastic types, with our work finding that PE MPs typically cracked into sharp fragments, while PS nanoplastics often fragmented into smoother, more curved shapes. These and other differences, along with interior and 3D surface images, provide new details on how the structure and aggregation of PE MPs and PS nanoplastics changes when degraded, which could influence how the resulting worn particles are collected or treated further.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".