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Record W2345525395 · doi:10.1002/chem.201601535

Sulfur and Nitrogen Codoped Carbon Tubes as Bifunctional Metal‐Free Electrocatalysts for Oxygen Reduction and Hydrogen Evolution in Acidic Media

2016· article· en· W2345525395 on OpenAlexaff
Tao Sun, Qiang Wu, Yue Jiang, Lei Du, Lijun Yang, Xizhang Wang, Zheng Hu

Bibliographic record

VenueChemistry - A European Journal · 2016
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsOverpotentialBifunctionalElectrocatalystProton exchange membrane fuel cellElectrolysisElectrolysis of waterSulfurChemistryInorganic chemistryOxygen evolutionCatalysisCarbon fibersChemical engineeringHydrogenMaterials scienceElectrochemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

The technological combination of fuel cells with water electrolysis is an ideal approach to address the problems of growing energy needs and environmental pollution, in which exploring cheap and stable electrocatalysts is the main challenge. Sulfur and nitrogen codoped carbon tubes (SNCTs), prepared by annealing the mixture of amorphous carbonaceous tubes and cysteine, are found to be active bifunctional metal-free electrocatalysts for both oxygen reduction and hydrogen evolution in acidic medium. The optimized SNCT catalyst exhibits a record high onset potential of 851 mV (vs. RHE) for oxygen reduction and concurrent a low overpotential of 76 mV for hydrogen evolution, with superior stability and low cost. The SNCT electrocatalyst could have great potential in proton exchange membrane fuel cells and water splitting devices.

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)0.001
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 score0.014
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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

Citations62
Published2016
Admission routes1
Has abstractyes

Explore more

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