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Record W2730172487 · doi:10.1039/c7sm00840f

Thermodynamics of chemical Marangoni-driven engines

2017· article· en· W2730172487 on OpenAlexaff
Rouslan Krechetnikov

Bibliographic record

VenueSoft Matter · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
Fundersnot available
KeywordsMarangoni effectThermodynamicsChemistryChemical physicsPhysicsSurface tension

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.226
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2017
Admission routes1
Has abstractyes

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