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Record W3008788451 · doi:10.3390/polym12020469

Influence of the Chitosan and Rosemary Extract on Fungal Biodegradation of Some Plasticized PLA-Based Materials

2020· article· en· W3008788451 on OpenAlexfundno aff
Elena Stoleru, Cornelia Vasile, Lăcrămioara Oprică

Bibliographic record

VenuePolymers · 2020
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsnot available
FundersOntario Ministry of Research and Innovation
KeywordsBiodegradationChitosanFood sciencePlasticizerChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The fungal degradation of the complex polymeric systems based on poly(lactic acid) (PLA) and natural bioactive compounds (chitosan and powdered rosemary alcoholic extract) was studied. Two fungal strains, Chaetomium globosum and Phanerochaete chrysosporium were tested. Both fungi characteristics and changes in morphology, structure and thermal properties were monitored. Biochemical parameters as superoxide dismutase, catalase, soluble protein and malondialdehyde have been determined at different time periods of fungal degradation. The fungi extracellular enzyme activities are slightly decreased in the case of composites containing bioactive compounds. The presence of natural compounds in the PLA-based polymeric system determines an acceleration of fungal degradation and probably the chemical hydrolysis, which further helps the attachment of fungi on the surface of polymeric samples. Significant decreases in average molecular mass of the polymeric samples were observed by fungi action; accompanied by structural changes, increase in crystallinity and decrease of thermal properties and the loss of the physical integrity and finally to degradation and integration of fungal degradation products into environmental medium. It was found that both fungi tested are efficient for PLA-based materials degradation, the most active from them being Chaetomium globosum fungus.

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 score0.010
Threshold uncertainty score0.384

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 head0.210
Teacher spread0.191 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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