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Mill Scales Blended Polymer Composites For Electrical Insulation Application

2018· article· en· W2899123394 on OpenAlexvenueno aff
Jayasmita Beura, Susanta Kumar Biswal, Saurabh Kundu, Tapan Kumar Rout

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2018
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsnot available
FundersInstitute of Minerals and Materials TechnologyTata Steel
KeywordsMaterials scienceComposite materialFourier transform infrared spectroscopyScanning electron microscopePolymerComposite numberChemical engineering

Abstract

fetched live from OpenAlex

Standalone composite films were prepared using modified polyester as a binder and waste iron oxides (mill scales) collected from a steel plant as inorganic filler. The morphology, structure, composition, strength and electrical insulation properties of polymer-iron composites were studied using various analytical techniques such as X-ray diffraction (XRD), Scanning electron microscope (SEM), Atomic force microscopy (AFM), optical microscopy, Fourier transform infrared (FTIR) spectroscopy, X-ray fluorescence (XRF), Brunauer–Emmett–Teller (BET) test, particle size analysis and electrical insulation test. The mill scales collected from the hot strip rolling mill (HSM) have found to comprise three different phases such as wustite, magnetite and hematite. Composites prepared using mill scales were showing three times higher strength compared to the mother polymer film. Electrical insulation of these composites were found to increase in the range of 55-230 MV/mm with increasing iron oxide content from 0.0125 g to 0.25 g in 2.5 g polymer. These results show a potential research field on the mill scales based composites for various advanced applications in improving insulation behaviour of materials which can withstand at higher temperatures and electrical stresses.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.013
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.376
Teacher spread0.346 · 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.

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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