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Record W4407566746 · doi:10.1002/cptc.202400377

Light Enhanced Water Dissociation in Bipolar Membranes

2025· article· en· W4407566746 on OpenAlexafffund
Patrick K Giesbrecht, Matthew J. Margeson, Mita Dasog, Michael S. Freund

Bibliographic record

VenueChemPhotoChem · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaResearch Nova ScotiaKillam Trusts
KeywordsDissociation (chemistry)MembraneChemistryMaterials sciencePhotochemistryChemical engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Bipolar membrane (BPM) integration can allow robust and abundant materials to be implemented into energy conversion frameworks and purification pathways vital for the development of clean technologies. However, large membrane voltages associated with water dissociation (WD) have hampered widespread adoption. This work investigates an alternative method to reduce the overvoltage associated with WD operation by illuminating the BPM interface in the presence of nanoparticulate catalyst layers that exhibit plasmonic character. The plasmonic character of the catalyst enhanced the field locally near the active catalyst site, lowering the resistance associated with WD as well as the onset potential for WD. Optimal catalyst loadings allowed a balance of light absorption, catalyst activity, and field utilization. Composites of known WD catalysts that exhibited minimal light activity with a plasmonic and WD active material exhibited improvements in the WD resistance and overvoltage of up to 20 % upon irradiation. This proof‐of‐concept work introduces a new paradigm for altering WD activity in BPMs, where the optical activity of WD catalysts can provide further tunability towards efficient WD and alternative energy conversion frameworks.

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)0.000
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 score0.034
Threshold uncertainty score0.316

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)0.0000.000

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 head0.002
GPT teacher head0.180
Teacher spread0.178 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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