Study of worm‐like micelles of alcohol propoxylated sulfate/alcohol ethoxylated surfactants mixtures for crude oil recovery
Bibliographic record
Abstract
Abstract Systems with elongated cylindrical micelles, also known as worm‐like micelles (WM) or viscoelastic surfactants (VES), have high surface activity and high viscosity, which makes them attractive in different applications such as improved oil recovery, friction reducing agents in heating and cooling fluids, household and personal care products. This study examines the carbon's chain length of alkyl propoxylated sodium sulfate anionic surfactants (C16O(PO)15S, C14O(PO)15S, and C12O(PO)15S) mixed with a nonionic ethoxylated surfactant on viscoelastic properties and interfacial tension. Results show that the larger the surfactant's carbon chain, the greater the possibility of forming WM. Furthermore, the propylene oxides' (PO) number and the nonionic surfactant's type of tail (linear or branched) is studied, not only on WM formation but also on WM/crude interfacial tension values. It is observed that the surfactant's molecular structure plays an important role in WM formation. By increasing the anionic surfactant's PO from 15 to 20 units, the maximum value of zero viscosity goes from 4.507 to 0.092 Pa.s and by changing the structure of the nonionic surfactant from linear (C12‐13EO8) to branched (C12‐14EO9) keeping the PO number of the extended surfactant constant, the zero viscosity value goes from 4.507 to 0.28 Pa.s. Likewise, the WM/crude interfacial tension reached values of the order of 10−3 mN/m in the salinity range studied, which makes these systems very interesting for polymer substitution in enhanced crude oil recovery (EOR).
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.
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) | 0.000 | 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".