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Record W2004364304 · doi:10.1380/ejssnt.2011.257

First Principles Study on the Adsorption and Dehydrogenation of Borohydride on Mn(111)

2011· article· en· W2004364304 on OpenAlexfundno aff
Ryan Lacdao Arevalo, Mary Clare Sison Escaño, Hideaki Kasai

Bibliographic record

Venuee-Journal of Surface Science and Nanotechnology · 2011
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsnot available
FundersScience Education Institute, Department of Science and Technology, Republic of the PhilippinesUniversity of British ColumbiaMinistry of Education, Culture, Sports, Science and Technology
KeywordsDehydrogenationAdsorptionBorohydrideMetastabilityDensity functional theoryChemistryMoleculeHydrogen bondAtom (system on chip)Computational chemistryHydrogen storageHydrogenHydrogen atomPhotochemistryInorganic chemistryCrystallographyPhysical chemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The mechanism of adsorption and dehydrogenation of borohydride (BH4) on Mn(111) is explored through first principles calculations within Density Functional Theory (DFT). It is found that the preferred sites for adsorption are the bridge site wherein the adsorbate dissociates resulting to BH2,ads+2Hads (“ads” means in the adsorbed state on the surface) fragments characterized by the competing dzz and dxz,yz interactions of the Mn-d states of the surface with the H-s and B-p states of the adsorbate, and the fcc hollow site wherein the adsorption is molecular. Water molecule is formed when a hydroxyl radical bonds with hydrogen atom on top of an initially adsorbed borohydride. It passes through a metastable state, then an intermediate state and finally the most stable state. [DOI: 10.1380/ejssnt.2011.257]

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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.045
GPT teacher head0.255
Teacher spread0.210 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

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