MétaCan
Menu
Back to cohort
Record W2972373289 · doi:10.1002/cjce.23645

Unsteady conjugate mass transfer of a 2D deformable droplet in a modest extensional flow in across‐slot

2019· article· en· W2972373289 on OpenAlexvenueno aff
Anjun Liu, Jie Chen, Zhenzhen Wang, Jingtao Wang, Zai‐Sha Mao, Chao Yang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsPéclet numberMass transferMechanicsMass diffusivitySherwood numberMass transfer coefficientThermodynamicsSchmidt numberThermal diffusivityFlow (mathematics)InterphaseMaterials sciencePhysicsConvectionTurbulenceNusselt number

Abstract

fetched live from OpenAlex

Abstract This work aims to investigate the unsteady conjugate interphase mass transfer between a stationary deformed drop and the modest extensional flow in a cross‐intersected 2D channel. It is very difficult to accurately quantify the transient mass transfer rate of solute in such a geometry. Therefore, we established a mathematical model on the basic of the Stokes equation and solved it by the boundary element method, which could deal precisely with a two‐phase flow system with a deformable interface; meanwhile, the convection‐diffusion equation was solved by the finite difference method to calculate the unsteady conjugate interphase mass transfer. The simulation results showed that the mass transfer rate, analyzed and characterized in terms of mean concentration variation and Sherwood number Sh, was affected by capillary number Ca, Peclet number Pe, viscosity ratio λ, interior‐to‐exterior diffusivity ratio K, distribution coefficient m, and wall effect factor W.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.194
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicInnovative Microfluidic and Catalytic Techniques InnovationFrench-language works237,207