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Record W2801711492 · doi:10.1002/elan.201800145

Powder Conductivity Assessment Using a Disposable 3D Printed Device

2018· article· en· W2801711492 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueElectroanalysis · 2018
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of WaterlooMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVan der Pauw methodMaterials scienceConductivityElectrical conductorComposite materialCarbon blackElectrical resistivity and conductivity3d printedElectrodeCarbon nanotubeBiomedical engineeringElectrical engineeringHall effectChemistry

Abstract

fetched live from OpenAlex

Abstract This work describes a low‐cost 3D printed apparatus developed for powder electrical conductivity measurement with electrodes fitted in either a two‐probe or four probe van der Pauw configurations. Electrical conductivity was then measured as a function of density by compressing the powders in an Instron mechanical test machine. Highly conductive carbon black, lower conductive Fe 3 O 4 and titania carbon nanotube composites were further tested under both methodologies to assess their reliability. Small powder masses are required for each measurement and our data matched well with literature values. It appeared that 3D printed polymer dies could be used to measure powder conductivity, though loss of material on the die walls was a source of error especially for small powder volumes.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0120.001

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 head0.032
GPT teacher head0.310
Teacher spread0.278 · 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