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Record W3116807798 · doi:10.1002/cctc.202001713

An In‐Depth Theoretical Exploration of Influences of Non‐Metal‐Elements Doping on the ORR Performance of Co−gN<sub>4</sub>

2020· article· en· W3116807798 on OpenAlexaff
Cehuang Fu, Liuxuan Luo, Lijun Yang, Shuiyun Shen, Xiaohui Yan, Jiewei Yin, Guanghua Wei, Junliang Zhang

Bibliographic record

VenueChemCatChem · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsLimitingDopingCatalysisMetalAdsorptionDensity functional theoryChemistryLimiting currentNanotechnologyMaterials scienceInorganic chemistryPhysical chemistryElectrochemistryComputational chemistryOrganic chemistryOptoelectronicsElectrodeEngineering

Abstract

fetched live from OpenAlex

Abstract Single atom catalysts (SACs) show a great attraction towards the oxygen reduction reaction (ORR) owing to its advantage in overcoming the cost issue of fuel cell because of its high utilization, low cost and great CO tolerance. However, theoretical investigation on the influences of different doping on the ORR catalytic activity of Co SACs has not been systematically performed. In this regard, the influences of non‐metal‐elements doping (B, N, Si, P, S) on the ORR catalytic activity of Co−gN 4 is well explored based on density functional theory (DFT). The rate‐limiting step for the ORR on Co−gN 4 is the formation of OOH. When the doping content of B increases, the adsorption on Co site becomes weaker which limits the occurrence of 4e − ORR process. N doping shows a weak promotion on the ORR process on Co site. The Si site next to N can be poisoned by OH. The P site next to N will be poisoned by O at high potential and OH at low potential. The S site next on N would be poisoned by O. It is revealed that the ORR process on Co site can be promoted when the carbon next to N is replaced by Si/P/S by promoting the rate‐limiting step.

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 score0.013
Threshold uncertainty score0.497

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 head0.021
GPT teacher head0.250
Teacher spread0.230 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

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