Experimental investigation of surface activity, micellization behaviour, and enhanced oil recovery performance of saponin‐based green surfactants
Bibliographic record
Abstract
Abstract In this study, three forms of saponin surfactants extracted from the Camellia sinensis plant (CS1, CS2, and CS3) were investigated. Related experiments were conducted to determine their characteristics and performance in the enhanced oil recovery (EOR) parameters like interfacial tension (IFT) and wettability. The crude plant extract cannot reduce the interfacial tension to suitable values for EOR, although it can create water‐wetting. The pure saponin has a better performance, and the IFT in its optimal concentration can reach lower values by adjusting the salinity. In addition to reducing IFT well to around 0.3 mN/m, which is considered suitable for EOR, the modified saponin was stronger in wettability alteration than the other two samples. The crude extract reached its lowest IFT of 7.582 mN/m at 3175 ppm, while the pure and modified saponin achieved minimum interfacial tensions of 1.646 and 0.375 mN/m at 1550 and 1125 ppm, respectively. The surfactants altered the wettability, however, the water‐wetting was different, so CS3, CS2, and CS1 had the greatest effect on wettability, respectively. The adsorption of CS1, CS2, and CS3 onto porous media was 26.5%, 12.4%, and 16.1%, respectively. Finally, the flooding of CS1, CS2, and CS3 solutions increased the recovery by 7.6%, 12.6%, and 15.8% OOIP, respectively.
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) | 0.001 | 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".