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Record W2808340276 · doi:10.1021/acsomega.8b01076

High Water Flux with Ions Sieving in a Desalination 2D Sub-Nanoporous Boron Nitride Material

2018· article· en· W2808340276 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS Omega · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversité de Sherbrooke
FundersUniversité de Rennes 1Centre National de la Recherche Scientifique
KeywordsMembraneDesalinationBoron nitrideReverse osmosisNanoporousMaterials scienceChemical engineeringOsmotic powerWater desalinationWater transportIonPolymerNanotechnologyForward osmosisChemistryEnvironmental engineeringEnvironmental scienceWater flowComposite materialOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Over the past decades, desalination by reverse osmosis (RO) membranes has attracted increasing attention. Although RO has proven its efficiency, it remains, however, relatively costly because of the use of high-pressure pumps and the low water permeability of conventional cross-linked polymer membranes. One route to improve the desalination performance consists of using membranes made from sub-nanoporous boron nitride (sNBN) monolayers. Indeed, by using molecular dynamics simulations, we report here that the water permeability of such sNBN membranes far exceeds that of conventional RO polymer membranes and is even higher than that of nanoporous graphene while the ion rejection remains close to 100%. At the same time, the molecular mechanism of water and ion transport through sNBN has been elucidated, with special attention paid to the impact of ions on water permeability through sNBN membranes.

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.

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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.216
Teacher spread0.208 · 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