Adsorption and dilational rheology of polyacrylamide at interfaces: Implication on the stability of polymer-containing foamulsions
Bibliographic record
Abstract
Abstract Understanding and developing a stable oil-laden foam (foamulsion) with targeted properties is important for many industrial applications including mineral flotation and resource recovery. In this work, the adsorption and interfacial viscoelasticity of polyacrylamide (PAM) and Triton X-100 (TX100) systems at the oil–water and air–water interfaces were investigated using dynamic tensiometry and dilational rheology measurements. For comparison, the TX100-alone systems were also examined at identical conditions. Pentane and dodecane were employed as representative oils. Dynamic surface/interfacial tension data for both mixed TX100/PAM and TX100-alone solutions could be well described by an empirical kinetic model. Although PAM itself in water was interface-inactive, the presence of TX100 surfactant with an onset concentration or above would induce the adsorption of PAM into the interfaces, as indicated by slower adsorption kinetic constant and higher viscoelastic moduli. A more concentrated surfactant in water was required for the oil–water interface than that for the air–water interface to trigger a significant impact of PAM on dynamic tensiometry and dilational viscoelasticity data. In addition, foam height decay profiles in oil–water mixture at two surfactant concentrations with or without PAM were monitored, from which the stability of foamulsion was quantified by foam half-life. It was found that at a low surfactant concentration, PAM had a negligible influence on the stability of foamulsions. However, PAM boosted the stability of foamulsions at relatively higher surfactant concentration (0.06 wt%), regardless of solvent types and contents. Correlation of foamulsion stability with interfacial tension and dilational viscoelasticity data is discussed. Graphical abstract
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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".