Adsorption mechanisms of emulsified crude oil droplets onto hydrophilic open-cell polymer foams
Bibliographic record
Abstract
Polyester/polyurethane open cell foam is examined for oil field water treatment. The oil droplet adsorption is influenced by foam-droplet bonding due to surface chemical interactions. Of the possible adsorption, acid-base, electrostatic (ES), and hydrophobicity interactions are among the most important. To determine the role of acid-base interactions on the adsorption, foam Ka and Kb values were measured. The foam is found to be a stronger base. To evaluate ES affect, adsorption experiments were performed for a series of emulsion pH levels using crude oil-in-water emulsions which were adjusted to acidic, neutral, and basic conditions. It was found that the turbidity removal rates (adsorption-separation quantification) were substantially increased during acidic conditions due to active interaction with the basic sites at the foam surface. Specifically, at pH5 the turbidity removal rates were faster perhaps due to optimal charge concentration and mobility of the ions where the electrostatic repulsion is minimal. At basic conditions, the turbidity removal rates were too slow could be due to electrostatic repulsive forces between hydroxyl ions-droplets and droplet-base foam. Based on the experimental results, to minimize the energy input, the separation process will be performed at pH5 where the turbidity rates are found to maximum.
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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) | 0.001 | 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".