MétaCan
Menu
Back to cohort
Record W4252523426 · doi:10.1002/9781118522318.emst085

Reverse Electrodialysis

2013· other· en· W4252523426 on OpenAlexaff
Odne Stokke Burheim, Jon G. Pharoah, David A. Vermaas, Bruno Bastos Sales, Kitty Nijmeijer, H.V.M. Hamelers

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsQueen's University
FundersNorges ForskningsrådEuropean Commission
KeywordsReversed electrodialysisSeawaterElectrodialysisMixing (physics)Renewable energyOsmotic powerEnvironmental scienceEnvironmental engineeringProcess engineeringMembraneChemistryOceanographyEngineeringGeologyPhysicsForward osmosisElectrical engineeringReverse osmosis

Abstract

fetched live from OpenAlex

Abstract Reverse Electrodialysis (RED) is an electrochemical membrane process that utilizes the free energy of mixing two solutions of different composition. The most commonly envisioned application is the mixing of seawater and river water at river deltas around the world. RED for mixing seawater and river water thus constitutes a renewable source for electric power production close to many urban regions. The worldwide potential is estimated to hold an average output in the order of as much as 2 TW. In this article, we have summarized the most important developments for RED since it was first discovered in 1954. RED represents a highly interdisciplinary research field and does not include membrane development alone. Challenges related to fluid mechanics, electrochemistry of electrodes, and traditional chemical engineering also constitute substantial parts of the research area for RED development. All these topics are discussed in this article.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.004

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 head0.005
GPT teacher head0.195
Teacher spread0.191 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicMembrane-based Ion Separation TechniquesFrench-language works237,207