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Record W1997022714 · doi:10.1039/c2dt32226a

Structure and stability of borohydride on Au(111) and Au<sub>3</sub>M(111) (M = Cr, Mn, Fe, Co, Ni) surfaces

2012· article· en· W1997022714 on OpenAlexaff
Ryan Lacdao Arevalo, Mary Clare Sison Escaño, Andrew Yu-Sheng Wang, Hideaki Kasai

Bibliographic record

VenueDalton Transactions · 2012
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
FundersMinistry of Education, Culture, Sports, Science and Technology
KeywordsBorohydrideAntibonding molecular orbitalAdsorptionMaterials scienceDensity functional theoryFermi levelAlloyCrystallographyMetalBand gapDensity of statesCatalysisPhysical chemistryComputational chemistryChemistryCondensed matter physicsMetallurgyPhysics

Abstract

fetched live from OpenAlex

We study the adsorption of borohydride on Au and Au-based alloys (Au(3)M with M = Cr, Mn, Fe, Co, and Ni) using first-principles calculations based on spin-polarized density functional theory. Favorable molecular adsorption and greater adsorption stability compared to pure Au are achieved on Au(3)M alloys. For these alloys, there is an emergence of unoccupied states in the surface d band around the Fermi level with respect to the fully occupied d band of pure Au. Thus, the derived antibonding state of the sp-d interaction is upshifted and becomes unoccupied compared to pure Au. The B-H bond elongation of the adsorbed borohydride on these alloy surfaces points to the role of surface-parallel (d(xy) and d(x(2)-y(2)) states) components of the d-band of the alloying metal M, most pronouncedly in the cases of M = Co or Ni. On the alloy surfaces, B binds directly with the alloying metal, unlike in the case of pure Au where the surface bonding is through the H atoms. These results pose relevant insights into the design of Au-based anode catalysts for the direct borohydride fuel cell.

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 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 score0.013
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 communication0.0000.000
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 head0.010
GPT teacher head0.221
Teacher spread0.212 · 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.

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

Citations38
Published2012
Admission routes1
Has abstractyes

Explore more

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