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Record W2748551682 · doi:10.14447/jnmes.v19i4.281

Electrocatalytic Properties of Egg-white Sol-gel Derived MnxFe3-xO4 (0 ≤ x ≤ 1.5) for Alkaline Water Electrolysis

2016· article· en· W2748551682 on OpenAlexvenueno aff
Narendra Kumar Singh, Ritu Yadav, Manish Yadav

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2016
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsEnthalpyOxygen evolutionElectrolysisTafel equationInorganic chemistryChemistryOxideElectrochemistryNuclear chemistryMaterials sciencePhysical chemistryElectrodeElectrolyteThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Some binary ferrites having molecular formula, MnxFe3-xO4 (0 ≤ x ≤ 1.5) have been synthesized by a simple and cost effective egg white sol-gel route. In each preparation, nitrates of constituent metal were taken as starting materials and egg white as gelling agent. The material, thus obtained was altered in the form of film electrode on the pre-treated Ni- support using an oxide-slurry painting tech-nique and investigated as electrocatalysts for the oxygen evolution reaction in KOH solutions. The study showed that Mn-substitution in the base oxide matrix influences the electrocatalytic activity of the material significantly and found to be greatest with 0.5 mol Mn-substitution. The reaction order has been found to be approximately unity with respect to OH¯ concentration. The Tafel slope values at low potential ranged between ~ 52 and ~ 65 mVdecade-1. The thermodynamic parameters namely, standard apparent electrochemical enthalpy of activa-tion (), standard enthalpy of activation () and standard entropy of activation () for the oxygen evolution reac-tion (OER) have also been determined. It has been observed that and values decreased with Mn-substitution in the Fe3O4 lattice. This decrease inandwas found to be greatest with 0.5 mol Mn. Thevalues were also found to be highly negative varying between ~ - 185 and – 211 J deg-1 mol-1.

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)0.000
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 score0.025
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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