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Record W2936950634 · doi:10.1021/acscatal.8b04873

ZnO As an Active and Selective Catalyst for Electrochemical Water Oxidation to Hydrogen Peroxide

2019· article· en· W2936950634 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 Catalysis · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Calgary
FundersBasic Energy SciencesStanford Woods Institute for the Environment
KeywordsCatalysisHydrogen peroxideElectrochemistryChemistryPhotochemistryInorganic chemistryElectrocatalystOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Electrochemical synthesis of hydrogen peroxide (H2O2) via two-electron water oxidation reaction (2e-WOR) is an ideal process for delocalized production for water cleaning and other applications. Previously reported water oxidation catalysts have limited activity and selectivity, imposing a bottleneck for broad adoption of this technology. We identify ZnO as a new stable, nontoxic, active, and selective catalyst for 2e-WOR to generate H2O2. Using density functional theory calculations, we propose that the (1010) facet of ZnO is an effective catalyst for 2e-WOR and confirm the prediction experimentally. We synthesize ZnO nanoparticles with a high fraction of (1010) facets and find that this catalyst gives an overpotential of 40 mV at 0.1 mA/cm2 and peak Faradaic efficiency of 81% toward H2O2 evolution.

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 categoriesMeta-epidemiology (narrow)
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 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 communication0.0000.001
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 head0.005
GPT teacher head0.221
Teacher spread0.216 · 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