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Optimization of Process Parameters for Generation of Nanocellular Polymer Foams

2013· article· en· W2033591733 on OpenAlexvenueno aff
Subhendu Bhattacharya, Rahul K. Gupta, Sati N. Bhattacharya

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials sciencePolypropyleneComposite materialSaturation (graph theory)NanocompositePolymerFoaming agentProcess optimizationChemical engineeringPorosity

Abstract

fetched live from OpenAlex

High melt strength polypropylene nanocomposites, PPNC/Cloisite 20A (clay) with exfoliated and intercalated morphologies were prepared and subsequently foamed in a batch setup under different foaming conditions. The foaming parameters were varied to relate the foam cell structure to these parameters and determine the efficiency of clay in producing fine cell foams. A Box Benkhen design approach was used initially to determine the effect of processing parameters on foam cell morphology and also to perform optimization studies. The optimization process helped in identifying the range of operating conditions needed to minimize foam cell sizes. Saturation pressure and temperature and foaming time and temperature are the four processing variables used in these studies. Nanocellular foam cells were effectively generated for the first time in Polypropylene nanocomposites.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.033
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.057
GPT teacher head0.359
Teacher spread0.301 · 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
Published2013
Admission routes1
Has abstractyes

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