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Record W2945242442 · doi:10.1039/c9cp00157c

First principles calculations of surface dependent electronic structures: a study on β-FeOOH and γ-FeOOH

2019· article· en· W2945242442 on OpenAlexaff
Yuki Sakamoto, Yusuke Noda, Kaoru Ohno, Kayo Koike, Katsushi Fujii, Tomiko M. Suzuki, Takeshi Morikawa, Shinichiro Nakamura

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInnovation Cluster (Canada)
FundersRIKEN
KeywordsCatalysisOxygen evolutionDensity functional theoryElectrochemistrySurface (topology)Electronic structureChemistryOxygenChemical engineeringChemical physicsNanotechnologyMaterials sciencePhysical chemistryComputational chemistryElectrodeOrganic chemistryGeometry

Abstract

fetched live from OpenAlex

We report a theoretical study on iron oxyhydroxide (FeOOH). The FeOOH surface is expected to act as an efficient electrochemical catalyst for the oxygen evolution reaction (OER), because it is based on iron, an element of the fourth highest Clarke number. Experimentally, the OER activity of β-FeOOH is known to be higher than that of γ-FeOOH. However, the details of the OER mechanism and the surface reactivities of the FeOOH polymorphs have not yet been fully understood. We performed first-principles calculations of bulk and surfaces of β-FeOOH and γ-FeOOH using density functional theory, to investigate their electronic structures and catalytic activities. The calculations suggest that depending on the surface indices, several surfaces may be favored for catalytic activities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.231
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations30
Published2019
Admission routes1
Has abstractyes

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