MétaCan
Menu
Back to cohort
Record W4310061699 · doi:10.1038/s41467-022-34884-4

Discovery of LaAlO3 as an efficient catalyst for two-electron water electrolysis towards hydrogen peroxide

2022· article· en· W4310061699 on OpenAlexafffund
Jihyun Baek, Jin Qiu, Nathan S. Johnson, Yue Jiang, Rui Ning, Apurva Mehta, Samira Siahrostami, Xiaolin Zheng

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Calgary
FundersDivision of Electrical, Communications and Cyber SystemsStanford Woods Institute for the EnvironmentCompute CanadaCanada First Research Excellence FundSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversity of CalgaryDivision of Emerging Frontiers in Research and InnovationNational Science Foundation
KeywordsHydrogen peroxideCatalysisElectrolysis of waterElectrolysisElectronChemistryHydrogenMaterials sciencePhysicsBiochemistryPhysical chemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Electrochemical two-electron water oxidation reaction (2e-WOR) has drawn significant attention as a promising process to achieve the continuous on-site production of hydrogen peroxide (H 2 O 2 ). However, compared to the cathodic H 2 O 2 generation, the anodic 2e-WOR is more challenging to establish catalysts due to the severe oxidizing environment. In this study, we combine density functional theory (DFT) calculations with experiments to discover a stable and efficient perovskite catalyst for the anodic 2e-WOR. Our theoretical screening efforts identify LaAlO 3 perovskite as a stable, active, and selective candidate for catalyzing 2e-WOR. Our experimental results verify that LaAlO 3 achieves an overpotential of 510 mV at 10 mA cm −2 in 4 M K 2 CO 3 /KHCO 3 , lower than those of many reported metal oxide catalysts. In addition, LaAlO 3 maintains a stable H 2 O 2 Faradaic efficiency with only a 3% decrease after 3 h at 2.7 V vs. RHE. This computation-experiment synergistic approach introduces another effective direction to discover promising catalysts for the harsh anodic 2e-WOR towards 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)0.001
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 score0.090
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.009
GPT teacher head0.269
Teacher spread0.260 · 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

Citations97
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicElectrocatalysts for Energy ConversionFrench-language works237,207