MétaCan
Menu
Back to cohort
Record W3102471459 · doi:10.3390/membranes10110340

Reduced Graphene Oxide-Based Foam as an Endocrine Disruptor Adsorbent in Aqueous Solutions

2020· article· en· W3102471459 on OpenAlexafffund
Jeanne N’Diaye, Sujittra Poorahong, Ons Hmam, Gastón Contreras Jiménez, Ricardo Izquierdo, Mohamed Siaj

Bibliographic record

VenueMembranes · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCentre québécois sur les matériaux fonctionnelsUniversité du Québec à Montréal
KeywordsAdsorptionGrapheneAqueous solutionChemical engineeringFourier transform infrared spectroscopyOxideMaterials scienceLangmuir adsorption modelBisphenol AFreundlich equationChemistryNanotechnologyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

A stable and magnetic graphene oxide (GO) foam-polyethyleneimine-iron nanoparticle (GO-PEI-FeNPs) composite has been fabricated for removal of endocrine disruptors-bisphenol A, progesterone and norethisterone-from aqueous solution. The foam with porous and hierarchical structures was synthesized by reduction of graphene oxide layers coupled with co-precipitation of iron under a hydrothermal system using polyethyleneimine as a cross linker. The presence of magnetic iron nanoparticles facilitates the separation process after decontamination. The foam was fully characterized by surface and structural scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy and X-ray photoelectron spectroscopy. The foam exhibits a high adsorption capacity, and the maximum adsorption percentages are 68%, 49% and 80% for bisphenol A, progesterone and norethisterone, respectively. The adsorption process of bisphenol A is explained according to the Langmuir model, whereas the Freundlich model was used for progesterone and norethisterone adsorption.

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 score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.299
Teacher spread0.259 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueMembranesSame topicGraphene research and applicationsFrench-language works237,207