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Mo<sub>2</sub>C-MoS<sub>2</sub> heterojunction a suitable and stable electrocatalyst for HER and supercapacitor Applications

2022· article· en· W4282000925 on OpenAlexaff
Rameez Ahmad Mir, Sanjay Upadhyay, Navpreet Kaur, O. P. Pandey

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsBrock University
Fundersnot available
KeywordsSupercapacitorTafel equationCapacitanceMaterials scienceHeterojunctionElectrochemistryElectrocatalystCapacitorGalvanic cellCurrent densityChemical engineeringAnalytical Chemistry (journal)ElectrodeOptoelectronicsChemistryElectrical engineeringVoltageMetallurgyPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract In the present work, Mo 2 C-MoS 2 heterojunction structures have been prepared by the wet impregnation method. For heterojunction species, the content of MoS 2 was varied from 5 to 20% by weight. The effect of the loading content of MoS 2 over Mo 2 C on hydrogen evolution reaction (HER) activity and electrochemical (EC) capacitance performance has been studied in detail. The Mo-C-S junction plays a key role in enhancing the HER activity and EC capacitance performance than the individual components. The prepared structures show enhanced HER activity with high current density ~36.2-78.3 mAcm -2 at very low applied potential (0.45 V), exhibiting a lower Tafel slope of 102.4 mVdec -1 . The EC capacitor performance determined by CV and galvanic charge-discharge (GCD) reveals that the synthesized samples exhibit an electrochemical double-layer capacitor (EDLC, C dl ) behavior. The higher EDLC (43.9 mFcm -2 ) and specific capacitance (2Fg -1 ) obtained for the synthesized samples determine their potential applicability for the new generation supercapacitors.

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 score0.036
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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