Interpreting Deposition Behavior of Polydisperse Surface-Modified Nanoparticles Using QCM-D and Sand-Packed Columns
Bibliographic record
Abstract
The rising use of surface-modified engineered nanoparticles (ENPs) will result in their increased presence in aquatic environments; hence, a better understanding of their environmental fate is needed. In this study, silicon nanocrystals (Si-NCs) capped with organic acids of varying alkyl-chain length were used as model functionalized ENPs. Particle deposition kinetics were evaluated using sand-packed columns and a quartz crystal microbalance with dissipation monitoring (QCM-D). In general, an increase in solution ionic strength resulted in increased particle deposition in both columns and the QCM-D. However, the overall trends in Si-NC deposition with regard to alkyl-chain length differed in the two experimental systems, revealing how the system geometry can play a key role in defining the contribution of different particle retention mechanisms. To interpret these differences in the Si-NC deposition behavior, multiple characterization techniques were used: dynamic light scattering, nanoparticle tracking analysis, scanning ion occlusion sensing, and laser Doppler velocimetry. QCM-D also revealed insights into the influence of the particle surface coatings on particle stability. The ratio of the two QCM-D output parameters revealed that the rigidity of the particle-collector interfacial bonds varied with the alkyl-chain length, whereby particles capped with longer alkyl chains were less rigidly attached to the silica surface. Moreover, it is shown that the interpretation of ENP deposition behavior using QCM-D is limited by the presence of large-particle aggregates (≥700 nm in this study) which do not fully couple to the QCM-D sensor. Under such conditions, QCM-D measurements of ENP deposition should be interpreted with caution as the microbalance response cannot be directly considered as deposited mass. This study improves our understanding of the role that surface modifiers and ENP aggregates play in ENP deposition kinetics in efforts to predict the transport and fate of ENPs in natural and engineered aquatic environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".