Back to the future: Fatty acids, the green genie to design smart soft materials
Bibliographic record
Abstract
Abstract Fatty acids are widely used in industries for various applications as soaps or in their crystalline form. Fatty acid soaps are used as surface‐active agent to stabilize foams and emulsions, for detergency, and surface wetting. Fatty acid molecules in organic solvents are used as low‐molecular‐mass organic gelators. Currently, there is a renewed interest in using fatty acids in industrial applications instead of petrochemical surfactants since fatty acid soaps are green surfactants. Fatty acids exhibit also many advantages, including the design of responsive soft‐materials. Fatty acid soaps are natural pH and thermoresponsive surfactants, which can lead to responsive foams and emulsions. In their crystalline form, fatty acid crystals in both aqueous and non‐aqueous solvents stabilize liquid foams. These crystals are also efficient in the production of liquid marbles. Fatty acids are solvosurfactants with the ability to form microemulsions and capillary suspensions. In this review, we illustrate how fatty acid molecules can be used in the manufacture of multiresponsive soft‐materials ranging from aqueous and non‐aqueous foams, emulsions, nanoemulsions, microemulsions, liquid marbles and capillary suspensions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".