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Record W2135186887 · doi:10.1002/app.31370

Postconsumer high‐density polyethylene/agave fiber foamed composites coated with chitosan for the removal of heavy metals

2009· article· en· W2135186887 on OpenAlexaff
Velazquez Herrera, César Gómez, Sergio Gómez‐Salazar, Denis Rodrigue, Rubén González‐Núñez, Gabriel Luna‐Bárcenas, Pierre Giovanni Mani-González, Alberto Herrera‐Gómez

Bibliographic record

VenueJournal of Applied Polymer Science · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceAdsorptionChitosanComposite materialPolyethyleneComposite numberScanning electron microscopeX-ray photoelectron spectroscopyHigh-density polyethylenePelletsChemical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Composites of postconsumer high‐density polyethylene with agave fiber were prepared by single‐screw extrusion with azodicarbonamide as a foaming agent to increase the surface area; the composite pellets were coated with chitosan afterward. A chemical pretreatment was applied to the pellets to enhance the chitosan gel compatibility. The adsorption capacities of the composites coated with chitosan were evaluated for Cd(II) and Cu(II) removal from aqueous solutions. The coated composites were characterized by scanning electron microscopy, attenuated total reflectance infrared spectroscopy, and X‐ray photoelectron spectroscopy. Atomic absorption spectroscopy was used to measure metal uptake in batch adsorption studies. The results of this study demonstrated the composites' ability to immobilize chitosan on their surfaces and their capacity to adsorb metal ions. The equilibrium isotherms for Cd(II) and Cu(II) adsorption on the chitosan‐coated composites were described by the Langmuir model. This material represents an attractive low‐cost recycled material for adsorbing metal ions from polluted waters. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010

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)0.001
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 score0.018
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations7
Published2009
Admission routes1
Has abstractyes

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