Deposition of TiO<sub>2</sub> Nanoparticles onto Silica Measured Using a Quartz Crystal Microbalance with Dissipation Monitoring
Bibliographic record
Abstract
Titanium dioxide (TiO2) nanoparticles introduced into subsurface environments may lead to contamination of drinking water supplies and can act as colloidal carriers for sorbed contaminants. A model laboratory system was used to examine the influence of water chemistry on the physicochemical properties of TiO2 nanoparticles and their deposition. Deposition rates of TiO2 particles onto a silica surface were measured over a broad range of solution conditions (pH and ionic strength) using a quartz crystal microbalance with energy dissipation monitoring (QCM-D). Higher particle deposition rates were observed under favorable interaction conditions (i.e., in the presence of attractive electrostatic interactions) in comparison to unfavorable deposition conditions where electrostatic repulsion dominates particle-surface interactions. Nanoparticle sizes were characterized by fluorescence correlation spectroscopy (FCS), dynamic light scattering (DLS), and atomic force microscopy (AFM). These analyses confirmed the nanoscale of the system under study as well as the presence of TiO2 aggregates in some cases. TiO2 deposition behavior onto silica measured using QCM-D was generally found to be in qualitative agreement with the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory of colloidal stability.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".