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Record W2314989700 · doi:10.1063/1.4942310

Foam injection molding of polypropylene/stainless steel fiber composites for efficient EMI shielding

2016· article· en· W2314989700 on OpenAlexaff
A. Ameli, Mohammadreza Nofar, Mehdi Saniei, S. Wang, Chul B. Park

Bibliographic record

VenueAIP conference proceedings · 2016
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsUniversity of TorontoPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceComposite materialPolypropyleneComposite numberMolding (decorative)Electromagnetic shieldingPercolation thresholdVoid (composites)MicrostructureBreakageCompression moldingFiberElectrical resistivity and conductivity

Abstract

fetched live from OpenAlex

Lightweight polypropylene/stainless-steel fiber (PP-SSF) composites with 15-35% density reduction were fabricated using foam injection molding and supercritical carbon dioxide (CO2). The electrical percolation threshold, through-plane electrical conductivity, and electromagnetic interference (EMI) shielding effectiveness (SE) of the PP-SSF composite foams were characterized and compared against the solid samples. The effects of the plasticizing gas and the void fraction on fiber breakage and orientation were also investigated. Microstructure characterization showed that the presence of dissolved CO2 decreased fiber breakage by about 30%, and together with foaming action, contributed to less preferential orientation of fibers. Consequently, the percolation threshold decreased up to four folds from 0.85 to 0.21 vol.% as the void fraction increased from 0 to 35%. The specific EMI SE was also significantly enhanced. A maximum specific EMI SE of 75 dB.g-1cm3 was achieved in PP-1.1 vol.% SSF composite foams, which was highly superior to 38 dB.g-1cm3 of the solid PP-1.0 vol.% SSF composites. The results reveal that light and efficient products with a lower fiber content can be developed by foam for EMI shielding applications.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.007
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)0.0010.000

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 head0.020
GPT teacher head0.253
Teacher spread0.232 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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