Formulating Nonionic Detergents <i>via</i> the Integrated Free Energy Model
Bibliographic record
Abstract
Abstract This work explores the optimum detergency conditions of alkyl ethoxylate (CXEOY) surfactants with the integrated free energy model (IFEM). IFEM is a molecular thermodynamic model that calculates the free energy of formation of oil‐swollen spherical micelles, with a core solubilization radius Ro, using surfactants from empty (oil‐free) micelles and oil molecules from a continuous oil phase. The described geometry allows for rapid calculations, using a personal laptop (3.4 GHz processor), where each solubilization energy profile (free energy vs. Ro curve) can be solved in 5 min or less. While previous work showed quantitative agreement between IFEM predictions and experimental solubilization of alkanes in CXEOY micelles, this work explores the possibility of using IFEM as a tool in surfactant selection. Experimental work has shown that detergency improved when operating near the phase inversion temperature (PIT) of the surfactant‐hexadecane system. The IFEM simulations in this work show, for the first time, that IFEM can be used to predict the PIT of surfactant‐oil systems, and that the surfactants selected via this method are consistent with the selection guided by experimental observations.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".