Using the Quartz Crystal Microbalance with Dissipation Monitoring to Evaluate the Size of Nanoparticles Deposited on Surfaces
Bibliographic record
Abstract
A quartz crystal microbalance with dissipation (QCM-D) monitoring can be an alternative tool to characterize nanoparticle size by virtue of its acoustic principle to sense adsorbed mass. In this study, sizes obtained by QCM-D for polymer-coated metallic nanoparticles and polydisperse polystyrene latex particle suspensions were compared with those obtained by transmission electron microscopy (TEM) and dynamic light scattering (DLS). We describe the obtained "QCM-D mass", which is weighted over surface area, by a general particle height distribution equation that can be used to determine the average particle diameter of a distribution of particles deposited on the QCM-D surface. Because the particle height distribution equation can be used for any particle geometry and surface packing geometry, it is described how the QCM-D can also be used to study the orientation of deposited nonspherical particles. Herein, the mean nanoparticle sizes obtained by QCM-D were generally in closer agreement with the primary particle size determined by TEM than with the sizes obtained by DLS, suggesting that primarily smaller particles within the particle population deposited on the sensor surface. Overall, the results from this study demonstrate that QCM-D could serve as an alternative and/or complementary means to characterize the size of nanoparticles deposited on a surface from suspensions of varying complexity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".