Toxicokinetics Explain Differential Freshwater Ecotoxicity of Nanoencapsulated Imidacloprid Compared to Its Conventional Active Ingredient
Bibliographic record
Abstract
Agricultural applications of nanotechnologies necessitate addressing safety concerns associated with nanopesticides, yet research has not adequately elucidated potential environmental risks between nanopesticides and their conventional counterparts. To address this gap, we investigated the risk of nanopesticides by comparing the ecotoxicity of nanoencapsulated imidacloprid (nano-IMI) with its active ingredient to nontarget freshwater organisms (embryonic Danio rerio, Daphnia magna, and Chironomus kiinensis ). Nano-IMI elicited approximately 5 times higher toxicity than IMI to zebrafish embryos with and without chorion, while no significant difference was observed between the two invertebrates. Toxicokinetics further explained the differential toxicity patterns of the two IMI analogues. One-compartmental two-phase toxicokinetic modeling showed that nano-IMI exhibited significantly slower elimination and subsequently higher bioaccumulation potential than IMI in zebrafish embryos (dechorinated), while no disparity in toxicokinetics was observed between nano-IMI and IMI in D. magna and C. kiinensis . A two-compartmental toxicokinetic model successfully simulated the slow elimination of IMI from C. kiinensis and confirmed that both analogues of IMI reached toxicologically relevant targets at similar levels. Although nanopesticides exhibit comparable or elevated toxicity, future work is of utmost importance to properly understand the life cycle risks from production to end-of-life exposures, which helps establish optimal management measures before their widespread applications.
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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) | 0.001 | 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".