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Record W4285173166 · doi:10.1039/9781839161186-00033

Evaporation of Ternary Sessile Drops

2022· book-chapter· en· W4285173166 on OpenAlexaff
Huanshu Tan, Christian Diddens, Xuehua Zhang, Detlef Lohse

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTernary operationSessile drop techniqueEvaporationMaterials scienceComputer scienceThermodynamicsPhysicsComposite materialContact angleProgramming language

Abstract

fetched live from OpenAlex

This chapter is concerned with drying ternary sessile droplets, where the three liquid components have different volatilities and mutual solubilities. The drying process can trigger concentration gradients within the droplet, leading to spontaneous phase separation and intense interfacial flows. We give a summary of several cases which we have introduced in previous work. To gain an insight into the physicochemical hydrodynamics, we also numerically simulate the droplet evaporation process. The ternary droplet discussed here can be seen as a basic model system for more complicated multicomponent droplet systems in practical applications, involved in material science, chemical engineering, or bioengineering.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.792
Threshold uncertainty score0.863

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)0.1450.001

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.

Opus teacher head0.030
GPT teacher head0.239
Teacher spread0.210 · 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 designBench or experimental
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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