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Record W1980366144 · doi:10.1002/aic.14422

Membrane distillation enhanced by an asymmetric electric field

2014· article· en· W1980366144 on OpenAlexaff
Jennifer Runhong Du, Wenlin Du, Xianshe Feng, Yufeng Zhang, Yi-Min Wu

Bibliographic record

VenueAIChE Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Waterloo
FundersBeijing Municipal Science and Technology CommissionTianjin Science and Technology CommitteeChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsElectric fieldPermeationMembrane distillationMembraneChemistryDesalinationFlux (metallurgy)ElectrodeVoltageAnalytical Chemistry (journal)Electric potentialChromatographyChemical engineeringElectrical engineeringOrganic chemistryPhysical chemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

A novel membrane distillation (MD) process enhanced by an asymmetric electric field was proposed. By applying an asymmetric electric field across a membrane with an increased field intensity in the direction of permeation, the permeation of water molecules through the membrane will be facilitated by the electric potential gradient, resulting in a higher flux than that achieved with the conventional process. The effectiveness of using asymmetric electric field to enhance mass transport was confirmed experimentally, and the effects of electric field parameters (including voltage, electrode distance, and electrode geometry) on water permeation were investigated. The novel process was tested for water desalination by MD and the results showed that both permeation flux and salt rejection were increased by the electric field. © 2014 American Institute of Chemical Engineers AIChE J , 60: 2307–2313, 2014

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.238
Teacher spread0.232 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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