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Record W3006665961 · doi:10.1149/1945-7111/ab6fee

Production of Hydrogen Peroxide for Drinking Water Treatment in a Proton Exchange Membrane Electrolyzer at Near-Neutral pH

2020· article· en· W3006665961 on OpenAlexafffund
Winton Li, Arman Bonakdarpour, Előd Gyenge, David P. Wilkinson

Bibliographic record

VenueJournal of The Electrochemical Society · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolysisChemistryCatalysisProton exchange membrane fuel cellHydrogen peroxideAnodeCathodeInorganic chemistryMembrane electrode assemblyElectrolyteElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

We provide a detailed report on the electrosynthesis of H 2 O 2 for drinking water treatment under near-neutral conditions using a proton exchange membrane (PEM) electrolyzer. Two novel cathode catalysts for O 2 electroreduction to H 2 O 2 were investigated in the PEM electrolyzer: an inorganic cobalt-carbon (Co–C) composite and an organic redox catalyst anthraquinone-riboflavinyl mixed with carbon (AQ–C), respectively. The impact of operational variables such as temperature, cathode carrier water flow rate, and anode configurations (aimed at mitigating carbon corrosion at the anode) were examined in single-pass and full recycle operation. Using a superficial current density of 245 mA cm −2 and an operating temperature of 40 °C, H 2 O 2 molar fluxes of 360 μ mol hr −1 cm −2 and 580 μ mol hr −1 cm −2 were generated at near-neutral pH with the Co–C and RF-AQ catalysts, respectively. Seventy-two hour experiments with closed loop recirculation, produced H 2 O 2 concentrations of 1300 and 3000 ppm for the Co–C and AQ–C catalysts, respectively. These concentrations are adequate for advanced oxidation (UV/H 2 O 2 ) treatment of drinking water, rendering the PEM electrolysis approach particularly suitable for on-site and on-demand production of H 2 O 2 .

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 score0.004
Threshold uncertainty score0.351

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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

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