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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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.238
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 teacher head, not a consensus.

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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