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Record W2002843614 · doi:10.1021/jp072450k

Studying Reduction in Solid Oxide Fuel Cell Activity with Density Functional Theory− Effects of Hydrogen Sulfide Adsorption on Nickel Anode Surface

2007· article· en· W2002843614 on OpenAlexaff
Natasha M. Galea, Eugene S. Kadantsev, Tom Ziegler

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2007
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAdsorptionHydrogen sulfideSulfurDissociation (chemistry)ChemistryInorganic chemistryHydrogenNickelSulfideOxideNickel sulfideElectrochemistryPhysical chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

To gain insight into the degree by which sulfur-based contaminants poison the solid oxide fuel cell (SOFC) anode, we examine adsorption and dissociation of consecutive molecules of hydrogen sulfide on a nickel (111) surface. Preferred adsorption sites, energies, transition states, and kinetic barriers are calculated for the resulting species, *SH x ( x = 0 − 2) and *H. Systematically larger amounts of adsorbed sulfur (0, 25, 50, 75, 100%) are calculated to determine the most energetically favorable sulfur surface coverage. The removal of existing sulfur surface atoms is studied to probe the irreversibility of the hydrogen sulfide adsorption reaction. The extent of molecular hydrogen adsorption at increasing surface sulfur coverages allows us to conclude that the presence of even 25% surface sulfur can reduce molecular hydrogen adsorption on the surface by half. Concurring with experimental data, our research demonstrates equilibrium coverage of 50% adsorbed sulfur on the surface. Due to the considerable exothermic nature of the hydrogen sulfide adsorption and dissociation reaction, partial irreversibility of the reaction is exhibited. This irreversibility proves challenging during attempts to remove surface sulfur and regain the original electrochemical activity.

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 score0.032
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.252
Teacher spread0.241 · 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

Citations33
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207