Revisiting the Thermodynamics of Water Surfaces and the Effects of Surfactant Head Group
Bibliographic record
Abstract
It is common knowledge that surfactants lower the surface tension of water. The typical textbook explanation of this phenomenon is that the force of attraction between surfactant and water molecules is less than that between two water molecules; hence the surface contraction force decreases in the presence of surfactants; however, this common description, based on the strength of intermolecular interactions, is overly simplified because it ignores an important thermodynamic function: the surface entropy of water. Here we report separate measurements of water's surface enthalpy and surface entropy in the presence of nonionic, zwitterionic, anionic, and cationic surfactants. While all of these surfactants decreased the surface enthalpy of water by 50-70%, the surface entropy of water could drop to near-zero or even negative values for ionic surfactants. Studies of this zero-entropy state of water surface using phase-sensitive sum-frequency generation (SFG) vibrational spectroscopy and molecular dynamics (MD) simulations suggested that the zero-entropy state of the water surface was associated with surfactant-induced ordering of water molecules and enhanced hydrogen bond formation at the water surface. Both effects reduce water molecules' degrees of freedom for motion and thus lower the surface entropy of water. The ability of a surfactant to decrease the surface entropy of water is in the order ionic > zwitterionic > nonionic. For all surfactant head groups surface entropy plays a critical role in determining the surface tension of water. The description of water's surface tension is not complete without considering its surface entropy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".