The Significance of Graphene Oxide-Polyacrylamide Interactions on the Stability and Microstructure of Oil-in-Water Emulsions
Bibliographic record
Abstract
The emulsification of oil in water by nanoparticles can be facilitated by the addition of costabilizers, such as polymers and surfactants. The enhanced properties of the resulting emulsions are usually attributed to nanoparticle/costabilizer synergy; however, the mechanism of this synergistic effect and its impacts on emulsion stability and microstructure remain unclear. Here, we study the synergistic interaction of graphene oxide (GO) and a high molecular weight anionic polyacrylamide (PAM) in stabilization of paraffin oil/water emulsion systems. We show that the addition of PAM reduces the amount of GO required to stabilize an emulsion significantly. In order to probe the synergistic effect of GO and PAM, we analytically analyze the oil-free GO and GO-PAM dispersions and directly image their morphology via Cryo-TEM and atomic force microscopy (AFM). X-ray diffraction results confirm the adsorption of PAM molecules onto GO sheets resulting in the formation of ultimate GO-PAM complexes. The adsorption phenomenon is a consequence of hydrogen bonding and acid-base interactions, conceivably leading to a resilient electron-donor-acceptor complex. The microstructure of emulsions is captured with two-color fluorescent microscopy and Cryo-TEM. The acquired images display the localization of GO-PAM complexes at the interface while large amount of GO-PAM flocs coexist at the interface and in between oil droplets. Localization of such complexes and flocs at the interface is found to be responsible for their slow creaming rates compared to their GO counterparts. Mechanical properties of both dispersions and emulsions are studied by shear rheology. Rheological measurements confirm that GO-PAM complexes have a higher desorption energy from the interface resulting in higher critical shear strain of GO-PAM emulsions. The results, with insights into both structure and rheology, form a foundational understanding for integration of other polymers and nanoparticles in emulsion systems, which enables efficient design of these systems for an application of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".