MétaCan
Menu
Back to cohort
Record W3096700006 · doi:10.1039/d0ra07629e

Large-pore-size membranes tuned by chemically vapor deposited nanocoatings for rapid and controlled desalination

2020· article· en· W3096700006 on OpenAlexaff
Mengfan Zhu, Yu Mao

Bibliographic record

VenueRSC Advances · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsStillwater (Canada)
FundersOklahoma Center for the Advancement of Science and TechnologyOklahoma State University
KeywordsMembraneDesalinationWettingMembrane distillationMaterials scienceChemical engineeringPermeationVapor pressureWater vaporChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Though membranes with pore size larger than 1 μm are much desired to increase the permeate flux of membrane distillation (MD), the vulnerability of large-pore-size membranes to pore wetting results in the penetration of saline water and consequent failure of MD operation. We report modification of large-pore-size membranes by chemically vapor deposited nanocoatings to achieve both high salt rejection and high permeate flux. The chemical vapor modification not only led to enhanced surface hydrophobicity and increased liquid entry pressure in membranes, but also significantly improved membrane wetting resistance at high temperature. Membranes with 1.0 and 2.0 μm pore size were successfully used for MD desalination with salt rejection higher than 99.99% achieved. Enlarging the pore size from 0.2 μm to 2.0 μm contributed to 48-73% enhancement in the permeate flux of the modified membranes. The modified large-pore-size membranes maintained the high permeate flux at elevated saline concentration and extended the operation time.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.050
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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.

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

Citations29
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRSC AdvancesSame topicMembrane Separation TechnologiesFrench-language works237,207