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Record W4220878500 · doi:10.1002/cctc.202101763

Enhancing Oxygen Reduction Reaction Activity Using Single Atom Catalyst Supported on Tantalum Pentoxide

2022· article· en· W4220878500 on OpenAlexafffund
Seoin Back, Amir Hassan Bagherzadeh Mostaghimi, Samira Siahrostami

Bibliographic record

VenueChemCatChem · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsTantalumCatalysisTantalum pentoxideOxygen reduction reactionElectrochemistryDensity functional theoryTransition metalChemistryPentoxideAtom (system on chip)Oxygen atomCombinatorial chemistryFuel cellsNanotechnologyMaterials scienceInorganic chemistryChemical engineeringPhysical chemistryComputational chemistryOrganic chemistryMoleculeVanadiumElectrode

Abstract

fetched live from OpenAlex

Abstract Transition metal oxides have emerged as promising cost‐effective alternatives to Pt catalysts for oxygen reduction reaction (ORR) in fuel cell applications. However, their low stability under harsh electrochemical conditions hinders their widespread applications. Tantalum pentoxide (Ta 2 O 5 ) has proven to be a stable material under ORR conditions, but its activity is limited. In this work, we incorporate single atom catalysts (SACs) in Ta 2 O 5 to resolve the limited ORR activity of this material by altering the electronic structures of surface Ta atoms. We use density functional theory (DFT) calculations to identify the most promising SACs with enhanced ORR activity. Pt, Rh, and Ir, are found to be the most promising SACs with improved ORR activity and high stability. This work suggests that SACs are effective in enhancing Ta 2 O 5 catalytic activities for ORR.

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 score0.010
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.239
Teacher spread0.219 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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