Water Spinach (Ipomoea aquatica F.) Effectively Absorbs and Accumulates Microplastics at the Micron Level—A Study of the Co-Exposure to Microplastics with Varying Particle Sizes
Bibliographic record
Abstract
The absorption of microplastics (MPs; size < 5 mm) by plants has garnered increasing global attention owing to its potential implications for food safety. However, the extent to which leafy vegetables can absorb large amounts of MPs, particularly those > 1 μm, remains insufficiently demonstrated. To address this gap in knowledge, we conducted water culture experiments using water spinach (Ipomoea aquatica F.) as a model plant. The roots of water spinach were exposed to a mixed solution that contained fluorescently labeled polystyrene (PS) beads with particle sizes of 200 nm and 1 μm for 10 d. We utilized laser confocal scanning microscopy and scanning electron microscopy to record the absorption, migration, and patterns of accumulation of these large particle sizes of MPs within water spinach. Our findings revealed that micron-sized PS beads were absorbed by the roots in the presence of submicron PS beads and subsequently transported through the exosomes to accumulate to significant levels in the leaves. Short-term hydroponic experiments further indicated that high concentrations of PS bead solutions significantly inhibited the growth of water spinach owing to their large specific surface area that hindered the uptake of water and nutrients by the roots. In conclusion, both sizes of PS beads were found to be absorbed by water spinach, thereby increasing the risk associated with direct human consumption of microplastics in fruits and vegetables. This study provides valuable scientific insights to assess the pollution risks related to fruits and vegetables, as well as ensuring vegetable safety.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".