Hazard Profiling of Commercially Relevant Quantum Dot Components Revealed Synergistic Interactions between Heavy Metals and Polymers
Bibliographic record
Abstract
Commercially used quantum dots (QDs) exemplify complex nanomaterials with multiple components, though little is known about the type of interactions between these components in determining the overall toxicity of this material. We synthesized and characterized a functional QD (CdSe/ZnS_P&E) that was identical in structure and composition to a patented and commercially applied QD and the combinations of its components (CdSe, CdSe/ZnS, ZnS, CdSe_P&E, ZnS_P&E, and P&E). Cells exposed to incremental concentrations of these materials were investigated for cell viability and cellular perturbations, contributing to a final common pathway of cell death using high-content screening assays in model human intestinal epithelial cells (HIEC-6). The concentrations that resulted in a loss of 20% cell viability (EC 20 values) for each tested component were used for estimating the combination index (CI) to evaluate synergistic or antagonistic effects between the components. Complete QD (core/shell-polymer) showed the highest toxic potential due to synergistic interactions between core and surface functional groups. The cationic polymer coating enhanced cellular uptake of the QD, ensuing lysosome acidification and release of heavy metal ions to the intracellular milieu, and caused oxidative stress and cytotoxicity. Overall, this study advances our understanding of the collective contribution of individual components of a functional QD toward its toxic potential and emphasizes the need to study multilayered nanomaterials in their entirety for hazard characterization.
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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".