Interfacial and Emulsion Characteristics of Oil–Water Systems in the Presence of Polymeric Lignin Surfactant
Bibliographic record
Abstract
It is hypothesized that polymeric lignin surfactants have different affinities for stabilizing oil–water emulsions and that the emulsifying performance of these surfactants is highly affected by their adsorption performance at the oil–water interface. To validate this hypothesis, the adsorption performance of sulfethylated lignin (SEKL) surfactant at different oil–water interfaces was examined by assessing the contact angle, dynamic interfacial tension, and surface loading (Γ). Moreover, the interfacial adsorption kinetics of SEKL was comprehensively assessed in different oil–water systems to reveal the mechanisms of the SEKL adsorption at the interface. Also, the impacts of SEKL concentration and ionic strength on the performance of SEKL as an effective emulsifier for the emulsions were assessed. Furthermore, the droplet size and instability index of the emulsions were systematically correlated with the adsorption performance of SEKL at the interface of oil and water. For the first time, by implementing a modified Ward Toradai diffusion model, two distinct early stages of the adsorption of SEKL at the oil interface were identified. Interestingly, the second stage was the determining stage of adsorption with the diffusion-controlled mechanism when polymers reconfigured at the oil–water interface. Salt screening facilitated the clustering of SEKL upon charge repulsion elimination, which removed the energy barrier in the first stage of adsorption (ΔEp→0 = 0), but it introduced a steric barrier upon the reconfiguration of polymers at the oil interfaces in the second stage of adsorption. In addition to the kinetics of adsorption, satisfactory correlations were observed between surface pressure (Δγ = γ∞ – γ0), surface loading (Γ) of polymers, and contact angle at oil interfaces on one hand and the oil droplet size and emulsion stability on the other hand.
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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".