Complexation of Surfactant/β‐Cyclodextrin to Inhibit Surfactant Adsorption onto Sand, Kaolin, and Shale for Applications in Enhanced Oil Recovery Processes. Part II: Dynamic Adsorption Analysis
Bibliographic record
Abstract
Abstract Surfactant adsorption onto solid surfaces is problematic in some industrial processes, such as in surfactant flooding for enhanced oil recovery. In this work, it was hypothesized that the use of a surfactant delivery system could prevent surfactant adsorption onto solid surfaces. Therefore, the encapsulation of sodium dodecyl sulfate (SDS) into the hydrophobic core of β‐cyclodextrin (β‐CD) to generate a surfactant delivery system (SDS/β‐CD) was evaluated in this work. This complexation was characterized using optical and scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FT‐IR). Dynamic adsorption evaluation was applied to determine the effectiveness of the complexation in inhibiting surfactant adsorption onto a variety of solid adsorbents including sand, and mixtures of sand–kaolin and sand–shale. Surfactant adsorption was also evaluated applying the quartz crystal microbalance technology (QCM‐D). The formation and morphology of the complexation was confirmed by optical microscopy, SEM, and FT‐IR. Dynamic adsorption tests demonstrated the effectiveness of the surfactant delivery approach in preventing the adsorption of surfactant (up to 74 % adsorption reduction). The QCM‐D technology confirmed these observations. Several mechanisms were proposed to explain the inhibition of surfactant adsorption including steric hindrance, self‐association of inclusion complexes, hydrophilicity increase, and disruption of hemimicelles formation.
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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".