Influence of Solution Chemistry on the Deposition and Detachment Kinetics of a CdTe Quantum Dot Examined Using a Quartz Crystal Microbalance
Bibliographic record
Abstract
Recent reports underline the potential environmental and public health risks linked to the "nano" revolution, yet little is known regarding the environmental fate and impacts of most nanomaterials following release in natural soils and groundwaters. Quantum dots (QDs) are one example of engineered nanomaterials that have been demonstrated to exhibit cytotoxic effects; hence the fate of this material in aqueous environments is of particular interest. In this study, a quartz crystal microbalance (QCM) was used to examine the interaction of a commercially available carboxyl terminated CdTe QD with a model sand (i.e., silica) surface. The deposition kinetics of the QD onto clean silica coated QCM crystals were measured over a wide range of solution conditions, in the presence of either monovalent (K+) or divalent cations (Ca2+). QD deposition rates onto silica were significantly greater in the presence of calcium versus potassium. Solution pH also influenced QD deposition behavior, with increased deposition observed ata lower pH value. The rate of QD release from the silica surface was also monitored using QCM measurements and found to be comparable to the rate of particle deposition when the monovalent salt was used. In contrast, the rate of QD release was considerably lower than the rate of deposition when particles were deposited in the presence of Ca2+. Physicochemical characterization of the QD suspended in varying electrolytes provided insights into the role of solution chemistry on particle size and electrophoretic mobility(surface charge). Measurements of QD size using dynamic light scattering (DLS) and transmission electron microscopy (TEM) were used to interpret the QD deposition behavior in different solution chemistries. Lower particle deposition rates observed at high ionic strengths were attributed to aggregation of the QDs resulting in decreased convective-diffusive transport to the silica surface.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".