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Record W2791551992 · doi:10.1002/9781119423829.ch11

On the Effect of Electric Field During Spark Plasma Sintering — A “faraday Cage” Approach

2018· other· en· W2791551992 on OpenAlexaff
Anil Prasad, Somi Doja, Lukas Bichler

Bibliographic record

VenueCeramic transactions /Ceramic transactions · 2018
Typeother
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceSpark plasma sinteringSinteringPelletsPelletElectric fieldCeramicComposite materialMetallurgyDiffractometerScanning electron microscope

Abstract

fetched live from OpenAlex

This chapter presents an experimental study in which an innovative method to investigate the effect of the electric field on a material during spark plasma sintering (SPS) processing was carried out. Graphite tooling consisting of copper and alumina layers enabled sintering of pure zinc oxide such that the effect of the electric field on the zinc oxide was manipulated. Raw zinc oxide powder obtained from Fisher Scientific was used for all experiments. The particle size distribution analysis for the raw powder was carried out using a laser diffractometer. The bulk density of the as-sintered pellet was determined via the Archimedes principle in distilled water. The densities and porosities of the two pellets were within the standard deviation, and thus the electric field had a minimal impact on the powder densification under the investigated SPS operating conditions. The chemical analysis results showed that there was a reduction in the oxygen composition after sintering; however, there was minimal difference in the composition of pellets produced with and without a Faraday cage. Sintering of pellets carried out with and without a Faraday cage revealed that the density remained nearly constant for both sintering conditions.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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