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Record W4409676399 · doi:10.1016/j.elstat.2025.104075

EHD-induced mass transfer from wet materials

2025· article· en· W4409676399 on OpenAlexaff
Alex Martynenko

Bibliographic record

VenueJournal of Electrostatics · 2025
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMass transferMaterials scienceEnvironmental scienceChemistryChromatography

Abstract

fetched live from OpenAlex

Electrohydrodynamics (EHD) is an interdisciplinary field that combines electrodynamics and fluid dynamics, primarily studying fluid motion driven by electric fields. The mechanisms of EHD arise from the interaction between the electric fields, charged particles and polarization effects within the fluid. This review extends the boundaries of our knowledge about mechanisms of EHD-induced mass transfer. The impact of EHD flow on convective and diffusive mass transfer in gaseous and liquid phases of capillary-porous materials is thoroughly discussed. The review covers the gap between electrohydrodynamics and thermodynamics, serving as a reference for future EHD applications in mass transfer processes. • EHD flow enhances both convective and diffusive mass transfer. • Conjugate effects of heat, momentum, and charge flows on mass transfer have been discussed. • The model of EHD-induced mass transfer was proposed. • The review connects domains of electrohydrodynamics and thermodynamics.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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
Published2025
Admission routes1
Has abstractyes

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