Bibliographic record
Abstract
The goal of this paper is to perform a general thermodynamic study of Marangoni-driven engines in which chemical energy is directly transformed into mechanical motion. Given that this topic has not been discussed before, we will explore here the most basic and fundamental aspects of the phenomena at work, which leads to a number of interesting observations typical of controversies in classical thermodynamics. Starting with a discussion of a few key motivating examples of chemical Marangoni-driven phenomena - tears of wine, an oscillating pendant droplet, "beating" oil lens, and traveling waves in a circular container - and contrasting homogeneous versus inhomogeneous thermodynamic systems we naturally arrive at alternative ways of storing and generating energy with the help of inhomogeneities in the bulk and surface properties of the working media. Of particular interest here are systems with interfaces - hence, in this context we discuss the nature and efficiency of the corresponding thermodynamic cycles leading to work done as a result of a non-uniform distribution of surface tension, which is in turn induced by a non-uniform surface active substance (surfactant) distribution, for both soluble and insoluble surfactants. Based on the relevant physical parameters of the working medium we can also evaluate the isothermality, i.e. temperature variations, dissipative losses, energy output and efficiency, entropy generation, and the period of such cycles in real processes. The role of singularity formation at the interface for the existence of such thermodynamic cycles is unraveled as well. Finally the discussion is concluded with a few ideas for potential applications of Marangoni-driven engines.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".