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Record W2099709515 · doi:10.1002/macp.201100090

Biodegradation of Thermoplastic Starch and its Blends with Poly(lactic acid) and Polyethylene: Influence of Morphology

2011· article· en· W2099709515 on OpenAlexaff
Gang Li, Pierre Sarazin, William J. Orts, Syed H. Imam, Basil D. Favis

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2011
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBiodegradationLow-density polyethyleneThermoplasticStarchPolylactic acidMaterials scienceMorphology (biology)PolyethyleneLactic acidGlycerolPolymer blendComposite materialChemical engineeringMineralization (soil science)Polymer chemistryCopolymerChemistryPolymerOrganic chemistryBacteria

Abstract

fetched live from OpenAlex

Abstract The room temperature mineralization of thermoplastic starch (TPS) with a high glycerol content and its blends with low‐density polyethylene (LDPE) and polylactic acid (PLA) are examined under controlled degradation conditions. These results are correlated with the morphologies and continuity behavior of the various blend systems. It is found that thermoplastic starch degrades more rapidly than native starch. Lowering the glycerol content in the TPS has virtually no effect on its biodegradation behavior. The only contribution to biodegradation of the TPS blend is from the TPS component. Blending TPS with LDPE and PLA in a co‐continuous morphology at a 50/50 composition provides a significant increase in TPS surface area, which increases the biodegradation rate for the blends as compared to pure TPS. The results indicate a close relationship between morphology, phase continuity, and biodegradation behavior. magnified image

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.202
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations38
Published2011
Admission routes1
Has abstractyes

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