Influence of Collector Surface Composition and Water Chemistry on the Deposition of Cerium Dioxide Nanoparticles: QCM-D and Column Experiment Approaches
Bibliographic record
Abstract
The deposition behavior of cerium dioxide (CeO(2)) nanoparticles (NPs) in dilute NaCl solutions was investigated as a function of collector surface composition, pH, ionic strength, and organic matter (OM). Sensors coated separately with silica, iron oxide, and alumina were applied in quartz crystal microbalance with dissipation (QCM-D) to examine the effect of these mineral phases on CeO(2) deposition in NaCl solution (1-200 mM). Frequency and dissipation shift followed the order: silica > iron oxide > alumina in 10 mM NaCl at pH 4.0. No significant deposition was observed at pH 6.0 and 8.5 on any of the tested sensors. However, ≥ 94.3% of CeO(2) NPs deposited onto Ottawa sand in columns in 10 mM NaCl at pH 6.0 and 8.5. The inconsistency in the different experimental approaches can be mainly attributed to NP aggregation, surface heterogeneity of Ottawa sand, and flow geometry. In QCM-D experiments, the deposition kinetics was found to be qualitatively consistent with the predictions based on the classical colloidal stability theory. The presence of low levels (1-6 mg/L) of Suwannee River humic acid, fulvic acid, alginate, citric acid, and carboxymethyl cellulose greatly enhanced the stability and mobility of CeO(2) NPs in 1 mM NaCl at pH 6.5. The poor correlation between the transport behavior and electrophoretic mobility of CeO(2) NPs implies that the electrosteric effect of OM was involved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 teacher head, 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".