Biological Uptake and Depuration of Radio-labeled Graphene by<i>Daphnia magna</i>
Bibliographic record
Abstract
Graphene layers are potential candidates in a large number of applications. However, little is known about their ecotoxicological risks largely as a result of a lack of quantification techniques in complex environmental matrices. In this study, graphene was synthesized by means of graphitization and exfoliation of sandwich-like FePO4/dodecylamine hybrid nanosheets, and (14)C was incorporated in the synthesis. (14)C-labeled graphene was spiked to artificial freshwater and the uptake and depuration of graphene by Daphnia magna were assessed. After exposure for 24 h to a 250 μg/L solution of graphene, the graphene concentration in the organism was nearly 1% of the organism dry mass. These organisms excreted the graphene to clean artificial freshwater and achieved roughly constant body burdens after 24 h depuration periods regardless of the initial graphene exposure concentration. Addition of algae and humic acid to water during the depuration period resulted in release of a significant fraction (>90%) of the accumulated graphene, but some still remained in the organism. Accumulated graphene in adult Daphnia was likely transferred to the neonates. The uptake and elimination results provided here support the environmental risk assessment of graphene and the graphene quantification method is a powerful tool for additional studies.
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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".