Yield stress of oxide dispersions—intermolecular forces of adsorbed small ionic additives and particle surface roughness
Bibliographic record
Abstract
Abstract The yield stress‐pH and zeta potential‐pH behaviour of α‐alumina and zirconia dispersions with adsorbed small ionic molecular additives such as phosphate and pyrophosphate were determined. The result for adsorbed citrate was included for comparison. Adsorbed phosphate at high surface coverage increased the maximum yield stress of low surface area α‐Al2O3 (AKP30 and AA07) dispersions slightly. This increase is attributed to the intermolecular hydrogen bonding between phosphates adsorbed on interacting particles. With high surface area ZrO2 (Tosoh) dispersions, however, the adsorbed phosphate decreased the maximum yield stress. This is due to its very rough surface morphology limiting the extent of intermolecular hydrogen bonding between adsorbed phosphate layers. Unlike phosphate, pyrophosphate reduces the maximum yield stress of AKP30 α‐Al2O3. This is due to the presence of intramolecular hydrogen bonding, thereby impeding effective bridging. A similar result is observed with citrate. The adsorbed pyrophosphate acts as an effective steric barrier keeping interacting particles further apart, thereby weakening the van de Waals attraction. These dispersions with the presence of non‐DLVO forces, that is bridging and steric, did not affect the linear relationship between yield stress and the square of the zeta potential as predicted by the yield stress–DLVO force model. However the relative importance of these non‐DLVO forces affect the value of the critical zeta potential at the point of transition from flocculated to dispersed state. © 2011 Canadian Society for Chemical Engineering
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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".