Application of the Hydrophilic–Lipophilic Deviation Concept to Surfactant Characterization and Surfactant Selection for Enhanced Oil Recovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it