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Record W1916019533 · doi:10.5539/jmsr.v4n3p40

Effect of Sintering Temperature on the Microstructure and Electrical Characteristics of Low Clamping Voltage Zinc Oxide- Based Ceramic Varistor

2015· article· en· W1916019533 on OpenAlexvenueno aff
J. Akinnifesi, Olumide Oluwole Akinwunmi

Bibliographic record

VenueJournal of Materials Science Research · 2015
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
FundersCentre for Engineering Research and Development
KeywordsMaterials scienceSinteringVaristorCeramicMicrostructureHomogeneity (statistics)ZincComposite materialCrystalliteGrain sizeDopingMetallurgyVoltageElectrical engineeringOptoelectronics

Abstract

fetched live from OpenAlex

Pelletized samples of polycrystalline zinc oxide ceramics doped with 2mol% MnO2 and 3mol% PbO were prepared by the conventional method of ceramic processing. The requisite composition was obtained by the direct mixing of constituent phases (DMCP) technique. Sintering was carried out at various temperatures ranging from 650oC to 850oC. The effects of sintering on the non-linear characteristics of the doped ceramic samples were investigated. The threshold or breakdown voltages were found to decrease as sintering temperature increased. Microstructural investigation revealed improved homogeneity and increased grain sizes with increasing sintering temperature. The random distribution of secondary phases also featured prominently. These findings were observed to correlate with the evolution of electrical characteristics while the sample sintered at 850oC exhibited the best electrical response suitable for varistor behaviour.

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 score0.003

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 head0.038
GPT teacher head0.333
Teacher spread0.294 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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