Application of the Hydrophilic–Lipophilic Deviation Concept to Surfactant Characterization and Surfactant Selection for Enhanced Oil Recovery
Bibliographic record
Abstract
Abstract The hydrophilic–lipophilic deviation (HLD) concept has been demonstrated to be useful in determining characteristic curvature (Cc) of a surfactant. Cc is a surfactant parameter that reflects the hydrophobicity/hydrophilicity or the tendency of the surfactant to form microemulsions in an oil–water system. In order for the Cc value to be calculated, the formation of the optimum Winsor III microemulsion of oil and water systems under specific salinity and temperature conditions is required. Surfactant Cc values have been widely used to quantitatively screen and select a suitable surfactant in formulations for different application areas, especially enhanced oil recovery (EOR). The HLD concept is an effective tool for designing new surfactant molecules to meet the target Cc value for a specific formulation condition. The HLD equation indicates the dependence of a microemulsion system on the changes of various system parameters. This article demonstrates how the HLD equation can be derived in different ways depending on the characteristics of the surfactant to identify the proper experimental approach so that the Cc values of different types of surfactants can be determined. Three types of surfactants were studied, including nonionic alcohol ethoxylates, anionic alkyl propoxy ethoxy sulfates, and carboxylates. The application of the HLD concept to surfactant selection for EOR application was also demonstrated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".