Transport, deposition and aggregation of metal oxide nanoparticles in saturated granular porous media: role of water chemistry, collector surface and particle coating
Bibliographic record
Abstract
As a multitude of engineered nanomaterials (ENMs) are being incorporated into a growing number of consumer products, the potential release of these reactive and potentially toxic constituents into natural aquatic environments and soils is inevitable. Nanosized metal oxides such as cerium dioxide (nCeO2), titanium dioxide (nTiO2) and zinc oxide (nZnO) are examples of ENMs currently appearing in consumer products. Upon the release of such ENMs into natural and engineered aquatic environments, particle aggregation and deposition behavior will determine the particle transport potential and thus the environmental fate and potential ecotoxicological impacts of the released materials. The objective of this research was to examine the transport behavior of select nanosized metal oxides (namely, nCeO2, nTiO2 and nZnO) in saturated granular porous media using laboratory-scale column experiments. The influence of water chemistry (pH, ionic strength (IS) and cation type (Na+, Ca2+, or Mg2+)) and particle coating (uncoated (bare) and poly(acrylic acid) (PAA)-coated ENMs) on particle deposition was examined in quartz sand or loamy sand-packed columns. Select particle transport studies in natural groundwater were also conducted, and PAA-coated metal oxide transport was compared to that of an analogous nanosized PAA polymeric capsule (nCAP). All ENM suspensions were characterized over a range of environmentally relevant water chemistries using dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) to establish aggregate size and laser Doppler velocimetry to determine particle surface potential. To investigate aggregate morphology, transmission electron microscopy (TEM) and scanning electron microscopy (SEM) images were obtained under select conditions. Overall, bare ENMs exhibited high retention within water-saturated quartz sand-packed columns at NaNO3 solution IS as low as 0.1 mM for nTiO2 and 0.01 mM for nZnO. Furthermore, bare nTiO2 was found to exhibit extensive aggregation, regardless of pH and IS. At lower salt concentrations, the particle attachment efficiency (α) for the nTiO2 aggregates onto quartz sand increased with increasing IS. At higher IS, α (pH 7) > α (pH 3) > α (pH 9), likely due to enhanced particle aggregation at pH 7 and subsequent physical straining within the granular matrix. Bare nTiO2 and nZnO displayed dynamic (time-dependent) deposition behaviors under selected conditions. In contrast, PAA-coated nTiO2 and nZnO were less prone to aggregation and exhibited significant transport potential at IS as high as 100 mM NaNO3 or 3 mM CaCl2. Likewise, PAA-coated nCeO2 particles suspended in NaNO3 were highly mobile in quartz sand-packed columns. Nonetheless, heightened nCeO2 and nCAP particle retention and dynamic transport behavior was observed with increasing divalent salt concentrations and in natural groundwater. Moreover, enhanced particle retention was encountered in loamy sand in comparison to quartz sand. Finally, the nCAPs proved to be a good surrogate particle for the PAA-coated nCeO2. These findings illustrate the importance of considering the extent and type of particle surface modification when investigating metal oxide contamination potential in granular aquatic environments. Furthermore, the results obtained emphasize the need to consider the nature of the granular medium, along with the water chemistry, when evaluating ENM contamination risks.
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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".