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Record W3007608841 · doi:10.1126/sciadv.aax5253

Capillary-driven desalination in a synthetic mangrove

2020· article· en· W3007608841 on OpenAlexafffund
Yunkun Wang, Jongho Lee, Jay R. Werber, Menachem Elimelech

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaShandong UniversityChina Scholarship CouncilYale UniversityNational Science Foundation
KeywordsDesalinationCapillary actionEvaporationEnvironmental scienceWater desalinationMangroveNanoporeMaterials scienceNanotechnologyEcologyChemistryMembraneBiologyMeteorologyPhysicsComposite material

Abstract

fetched live from OpenAlex

According to the cohesion-tension theory, mangrove trees desalinate salty water using highly negative pressure (or tension) that is generated by evaporative capillary forces in mangrove leaves. Here, we demonstrate a synthetic mangrove that mimics the main features of the natural mangrove: capillary pumping (leaves), stable water conduction in highly metastable states (stem), and membrane desalination (root). When using nanoporous membranes as leaves, the maximum osmotic pressures of saline feeds (10 to 30 bar) allowing pure water uptake precisely correspond to expected capillary pressures based on the Young-Laplace equation. Hydrogel-based leaves allow for stable operation and desalination of hypersaline solutions with osmotic pressures approaching 400 bar, fivefold greater than the pressure limits of conventional reverse osmosis. Our findings support the applicability of the cohesion-tension theory to desalination in mangroves, provide a new platform to study plant hydraulics, and create possibilities for engineered membrane separations using large, passively generated capillary pressures.

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 score0.002

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)0.0000.000

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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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

Citations94
Published2020
Admission routes2
Has abstractyes

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