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Record W2890427150 · doi:10.1002/aenm.201801461

Highly Efficient, Biochar‐Derived Molybdenum Carbide Hydrogen Evolution Electrocatalyst

2018· article· en· W2890427150 on OpenAlexafffund
Govinda Humagain, Kevin MacDougal, Judy MacInnis, James M. Lowe, Robert H. Coridan, Stephanie MacQuarrie, Mita Dasog

Bibliographic record

VenueAdvanced Energy Materials · 2018
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsCape Breton UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityCanada Foundation for Innovation
KeywordsMaterials scienceElectrocatalystBiocharCatalysisChemical engineeringHydrogen productionRenewable energyWater splittingElectrochemistryHydrogenFuel cellsNanotechnologyPhotocatalysisElectrode

Abstract

fetched live from OpenAlex

Abstract The establishment of an economic means of hydrogen production by electrochemical water‐splitting can help alleviate the intermittency problem associated with renewable energy sources such as solar and wind, and also provide for the extensive commercialization of fuel cell technologies. To enable this, cheap, active, and stable hydrogen evolution catalysts that can replace precious metal catalysts such as Pt must be developed. Herein, a scalable synthesis of porous Mo 2 C nanostructures derived from biochar, an inexpensive plant byproduct, is reported. The Mo 2 C catalyst materials prepared using this solid‐state method, loaded on planar substrates, require overpotentials of only 35 and 60 mV to drive current densities of −10 and −100 mA cm −2 in 0.50 m H 2 SO 4 solution and exhibit stable operation for >100 h at operating current densities of −10 and −100 mA cm −2 .

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), Insufficient payload (model declined to judge)
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 score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.213
Teacher spread0.208 · 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

Citations103
Published2018
Admission routes2
Has abstractyes

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