Bibliographic record
Abstract
The objectives of this work were to develop a simple procedure to determine the hydrophilic/lipophilic nature of surfactants based on microemulsion phase behavior and to characterize mixed surfactant systems. The concept of hydrophilic-lipophilic difference (HLD) and the net-average curvature (NAC) were used to develop a model to characterize systems containing anionic-anionic and anionic-nonionic surfactant mixtures. Salinity and temperature scans were carried out and phase changes were noted. It was found that in anionic-anionic systems the linear mixing rule is applicable, and was used to calculate the intrinsic curvature parameter (Ci), which can be used as an alternative scale to the HLB and packing factor to characterize the behavior of a surfactant in microemulsion systems. For anionic-nonionic systems the linear mixing rule does not apply because of the interactions between anionic and nonionic surfactants which seem to be dominated by the charge shielding effect that nonionic surfactants have on anionic surfactants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".