Understanding the surface properties and rheology of a silica suspension mediated by a comb-type poly(acrylic acid)/poly(ethylene oxide) (PAA/PEO) copolymer: effect of salinity
Bibliographic record
Abstract
Understanding the surface properties and rheology of colloidal suspensions in the presence of polymer additives with high salinity are of great importance in formulating construction materials and optimizing process conditions in the mining and petroleum industry. In this work, the surface properties and rheology of a model spherical silica aqueous suspension mediated by a comb-type poly(acrylic acid)/poly(ethylene oxide) (PAA/PEO) copolymer at various salt concentrations have been investigated. Adsorption measurements using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) align well with zeta potential tests and show that polymer adsorption on silica surfaces is enhanced at high salinity (i.e., 3 M NaCl) than at low salinity (i.e., 1 mM NaCl) due to the suppression of the electrical double layer. Surface Forces Apparatus (SFA) measurements reveal that for interactions between two mica surfaces (the basal plane of which has a similar structure as silica) at a high polymer concentration (e.g., 2 wt%), steric repulsion dominates in 1 mM NaCl while bridging attraction is observed in 3 M NaCl. Surface force measurements agree with rheological results on silica suspensions with 0.5 to 2 wt% of PAA/PEO addition, which shows a significant decrease in yield stress in 1 mM NaCl due to steric repulsion but an insignificant variation in yield stress in 3 M NaCl due to attractive bridging interactions. This work provides useful information regarding the surface properties and rheological properties of comb-type polymer-mediated silica suspensions under different salinity conditions, with implications on designing and processing complex colloidal suspensions with polymer additives for various applications.
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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".