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Record W830967784 · doi:10.1063/1.4918502

Effect of maleated polypropylene emulsion on the mechanical and thermal properties of lignin-polypropylene blends

2015· article· en· W830967784 on OpenAlexaff
Mohamed A. Abdelwahab, Manjusri Misra, A. N. Mohanty

Bibliographic record

VenueAIP conference proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthPolypropyleneLigninFlexural strengthComposite materialPolyolefinPolymerEmulsionPolymer blendChemical engineeringOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

The increasing oil rates and environmental concerns of the use of synthetic or petroleum-based polymers has newly led to a growing attention in eco-friendly materials. Lignin has received much attention as a novel eco-friendly material due to its abundant availability and its potential as a low-cost filler. Biobased blends from polypropylene (PP) and lignin were fabricated by extrusion followed by injection moulding. In order to improve the compatibility of the polar lignin and the non-polar matrix PP, three different maleated PP emulsions, namely ME91735 (nonionic PP emulsion), ME42035 (cationic water based emulsion of polyolefin waxes) and PP286 (containing 1-5% N,N-ethylethanolamine) were used as coupling agents. The mechanical properties such as tensile and flexural strength as well as tensile and flexural modulus of the blends were improved by using lignin treated with 2.5 wt.% of the emulsions. However, the elongation at break decreased in the case of the lignin treated with ME91735 and ME42035 as compared to the untreated lignin. The morphological and thermal properties of the blends were also studied.

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 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 score0.006
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.019
GPT teacher head0.211
Teacher spread0.192 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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