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Record W2886209260 · doi:10.14447/jnmes.v21i3.406

Surfactant-assisted Pulse Electrodeposition of Hausmannite Nano-rods/particles with Improved Pseudocapacitive Performance

2018· article· en· W2886209260 on OpenAlexvenueno aff
Mustafa Aghazadeh

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceCyclic voltammetryChemical engineeringSupercapacitorElectrodeNuclear chemistryNanotechnologyElectrochemistryChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Pulse cathodic electrodeposition of manganese oxide was performed from 0.005M MnCl2 in the presence of cetyl trimethylammonium bromide (CTAB). The electrosynthesis experiments were done at the simple pulse mode (ton=1ms and toff=1ms) with applying current density of 10 mA cm–2. The structural analyses through XRD and FTIR revealed that the prepared sample have the pure hausmannite crystal structure. Morphological observations by SEM showed that the deposited hausmannite has mixed rod/particle morphology. The charge storage performances of the prepared hausmannite were evaluated by cyclic voltammetry and charge-discharge techniques. The Mn3O4 working electrode exhibited only 13% capacity decay after 1000 cycles at the current load of 2 A g–1, and its SC value reduced from 239 F g–1 to 207 F g–1. The results proved that the prepared Mn3O4 nano-rods/particles are suitable electrode material for 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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