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Record W2328708878 · doi:10.1021/am503242v

Graphene Oxide as a Water Dissociation Catalyst in the Bipolar Membrane Interfacial Layer

2014· article· en· W2328708878 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Materials & Interfaces · 2014
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of Manitoba
KeywordsOverpotentialMaterials scienceGrapheneMembraneOxideDissociation (chemistry)CatalysisElectrodialysisChemical engineeringLayer (electronics)Inorganic chemistryNanotechnologyElectrodeChemistryOrganic chemistryElectrochemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Bipolar membranes are formed by the lamination of an anion- and cation-exchange layer. Upon a sufficient applied reverse bias, water molecules at the layer junction dissociate, generating OH(-) and H(+), which can be useful in electrodialysis and electrosynthesis applications. Graphene oxide has been introduced into bipolar membrane junctions (illustrated in the adjacent graphic) and is shown to be an efficient new water dissociation catalyst, lowering the overpotential by 75% compared to a control membrane. It was found that adjusting deposition conditions changes the nature of the graphene oxide films, leading to tunable membrane performance. Additionally, it is shown that their low overpotentials are stable, making for industrially viable, high-performance bipolar 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)0.001
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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