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Record W3084136621 · doi:10.3389/fchem.2020.00733

Nitrogen-Doped Oxygenated Molybdenum Phosphide as an Efficient Electrocatalyst for Hydrogen Evolution in Alkaline Media

2020· article· en· W3084136621 on OpenAlexfundno aff
Muhammad Waqas Khan, Suraj Loomba, Rashad Ali, Md Mohiuddin, Ahmed Alluqmani, Farjana Haque, Yongkun Liu, Rizwan Ur Rehman Sagar, Ali Zavabeti, Turki Alkathiri, Babar Shabbir, Xian Jian, Jian Zhen Ou, Asif Mahmood, Nasir Mahmood

Bibliographic record

VenueFrontiers in Chemistry · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsnot available
FundersRMIT UniversitySaudi Arabian Cultural MissionOntario Ministry of Natural Resources and ForestryAustralian National Fabrication Facility
KeywordsPhosphideOverpotentialElectrocatalystMolybdenumInorganic chemistryMaterials scienceHydrogenExchange current densityChemistryOxideElectrolyteChemical engineeringCatalysisElectrodeElectrochemistryTafel equationMetallurgyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Phosphides of transition metals (TMPs) are a developing class of materials for hydrogen evolution reaction (HER) as an alternative to expensive noble metals to produce clean energy. Herein, the nitrogen-doped molybdenum oxide (MoOx) are developed via a facile and simple hydrothermal method, followed by annealing in N2 atmosphere and phosphorization to form nitrogen-doped oxygenated molybdenum phosphide (N-MoP) sphere-shaped structure. The developed N doped phosphide structure depicts enhanced HER activity by reaching a current density of 10 mA cm-2 at a very low overpotential of only 87 mV which is much better than annealed nitrogen-doped molybdenum oxide (A-MoOx) 138 mV in alkaline medium. The N-MoP is a highly efficient electrocatalyst for HER attributed to a more exposed surface, large electrode/electrolyte interface and appropriate binding energies for reactants. This study extends the opportunity of developing nitrogen-doped TMPs, which can display exceptional properties as compared to their oxides.

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 score0.049
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.210
Teacher spread0.203 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

Explore more

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