MétaCan
Menu
Back to cohort
Record W3047689155 · doi:10.14447/jnmes.v23i1.a01

To Study the Effect of LiMn2O4, Nanofibers of LiMn2O4, and Graphene/Polyaniline/Carbon Nanotube as Electrode Materials in the Fuel Cell

2020· article· en· W3047689155 on OpenAlexvenueno aff
Kabbir Ali, Hafiz Ahmad Raza, Muhammad Faizan Malik, Sami M. Ibn Shamsah, Riffat Amna, Amina Sarfraz

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2020
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsnot available
Fundersnot available
KeywordsGrapheneCarbon nanotubeMaterials scienceElectrodePolyanilineNanofiberCarbon nanofiberChemical engineeringNanotubeFuel cellsNanotechnologyComposite materialChemistryPolymer

Abstract

fetched live from OpenAlex

This study is conducted to explore the best electrodes materials for energy conversion devices. The two cathode materials (LiMn2O4 and nanofibers of LiMn2O4) and the anode material (graphene/polyaniline/carbon-nanotube) were synthesized by a wet chemical method which includes sol-gel and chemical polymerization techniques. The prepared materials were characterized by SEM, XRD FTIR, and cyclic voltammetry. The characterization results show that LiMn2O4 exhibits a porous and hollow structure, which improves the utilization of the active mass area, and allows the dual conduction of Li+ and electrons, which effectively relieves the structural strain and volume change. The cyclic voltammetry results record that LiMn2O4 and nanofibers of LiMn2O4 as a cathode material enhanced the cycling performance and possess excellent stability. Further, the conductivity of each sample was measured using the DC four-probe method, and the highest conductivity was observed for the LiMn2O4 nanofibers 1.43 SCm -1 at 650.

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)0.004
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 score0.008
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.268
Teacher spread0.256 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of New Materials for Electrochemical SystemsSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207