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Record W2939362103 · doi:10.1002/chem.201901215

Benchmarking Three Ruthenium Phosphide Phases for Electrocatalysis of the Hydrogen Evolution Reaction: Experimental and Theoretical Insights

2019· article· en· W2939362103 on OpenAlexaff
Tingting Liu, Jianmei Wang, Changyin Zhong, Shiyu Lu, Wenrong Yang, Jian Liu, Weihua Hu, Chang Ming Li

Bibliographic record

VenueChemistry - A European Journal · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersFundamental Research Funds for the Central Universities
KeywordsPhosphideRutheniumOverpotentialGrapheneElectrocatalystElectrochemistryMaterials scienceHydrogenCatalysisChemical engineeringInorganic chemistryNanotechnologyChemistryPhysical chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The outstanding electrocatalytic activity of ruthenium (Ru) phosphides toward the hydrogen evolution reaction (HER) has received wide attention. However, the effect of the Ru phosphide phase on the HER performance remains unclear. Herein, a two‐step method was developed to synthesize nanoparticles of three types of Ru phosphides, namely, Ru 2 P, RuP, and RuP 2 , with similar morphology, dimensions, loading density, and electrochemical surface area on graphene nanosheets by simply controlling the dosage of phytic acid as P source. Electrochemical tests revealed that Ru 2 P/graphene shows the highest intrinsic HER activity, followed by RuP/graphene and RuP 2 /graphene. Ru 2 P/graphene affords a current density of 10 mA cm −2 at an overpotential of 18 mV in acid media. Theoretical calculations further showed that P‐deficient Ru 2 P has a lower free energy of hydrogen adsorption on the surface than other two, P‐rich Ru phosphides (RuP, RuP 2 ), which confirms the excellent intrinsic HER activity of Ru 2 P and is consistent with experiment results. The work reveals for the first time a clear trend of HER activity among three Ru phosphide phases.

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 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 score0.010
Threshold uncertainty score0.580

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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

Citations54
Published2019
Admission routes1
Has abstractyes

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