Transport and deposition of quantum dots and model polystryene nanoparticles in granular aquatic environments
Bibliographic record
Abstract
Quantum dots (QDs) are luminescent semiconductor nanoparticles with relevant applications in different fields, including medical imaging, solar cells, and sensors. However, toxic effects in living organisms have been reported, and upon release, the potential ecotoxicological risks of QDs will be directly related to their transport and fate. The objective of this research was to evaluate the transport and deposition of different QDs in systems representative of natural subsurface environments and engineered granular filtration processes. Two experimental approaches were used: (i) laboratory scale columns packed with granular materials representative of the soil or filter matrix, and (ii) a quartz crystal microbalance with dissipation monitoring (QCM-D) using sensors coated with materials representative of grain-water interfaces. The transport and deposition of the QDs were determined over a broad range of solution chemistries (i.e., ionic strength, pH, cation type, natural organic molecules (NOM)). In all cases, the deposition experiments were complemented with an appropriate physicochemical characterization of the particles and collectors. In experiments conducted with packed columns, the transport potential of a CdSe QD, a CdTe QD and model nanosized polystyrene particles was systematically investigated in two water-saturated granular matrices: (i) clean quartz sand and (ii) loamy sand obtained from Québec farm. This study provided a good starting point for the comparison of the transport behavior of engineered nanoparticles in quartz sand versus soil matrices (loamy sand), where greater retention was observed. The results obtained suggest that differences in retention are likely related to the binding affinity of surface-modified nanoparticles for specific soil constituents.In experiments conducted with a QCM-D, the deposition kinetics of polymer-coated QDs were compared with those measured for two different polystyrene latex nanoparticles onto model environmentally relevant collector surfaces (SiO2, Al2O3, or Al2O3 coated with NOM). The results showed that QD retention is relatively low compared to that of polystyrene latex particles, and in the presence of NOM, significantly lower deposition rates of QDs were observed. Overall, the data suggested that these phenomena could be attributed to the surface coating (polymers) used to stabilize the QDs, likely due to "electrosteric repulsion". In the final series of experiments, the deposition kinetics of functionalized silicon-nanocrystals (Si-NCs) was compared by means of: columns packed with quartz sand, and a QCM-D with SiO2 coated crystals (as a model sand surface). The Si-NCs used here were functionalized with carboxylic acids of varying alkyl-chain length, and in general, experiments conducted with both techniques revealed that the mobility of Si-NCs increases with longer alkyl-chains. QCM-D provided further insight on the nanoparticle deposition behavior, whereby the output parameters (i.e., frequency and dissipation) indicated how rigidly the ENPs are bound to the surface. Yet, the interpretation of nanoparticle deposition behavior by QCM-D may be limited by the size of the particle assessed; as it was determined that in the presence of large aggregates, the acquired frequency shifts were not proportional to the deposited mass.
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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".