MétaCan
Menu
Back to cohort
Record W2332433438 · doi:10.1021/acs.iecr.5b00748

Introduction of an Enhanced Binding of Reduced Graphene Oxide to Polyurethane Sponge for Oil Absorption

2015· article· en· W2332433438 on OpenAlexafffund
Ricky Tjandra, Gregory Lui, Alicia Veilleux, Josh Broughton, Gordon Chiu, Aiping Yu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceGovernment of Ontario
KeywordsGrapheneTriethoxysilaneAbsorbanceMaterials sciencePolyurethaneOxideChemical engineeringFourier transform infrared spectroscopyScanning electron microscopeNuclear chemistryNanotechnologyChemistryComposite materialChromatography

Abstract

fetched live from OpenAlex

Reduced graphene oxide (rGO) was covalently bound onto the surface of polyurethane (PU) sponge using two different coupling agents: (3-aminopropyl)triethoxysilane and titanium(IV)(triethanolaminato)isopropoxide. The coupling agents were introduced before and after reduction of graphene oxide to compare the impact of the sequence on the absorbance capacities. The sponges were evaluated based on hydrophobicity and pump oil absorbance capacities. The structure and properties of the sponges were evaluated using scanning electron microscopy and Fourier transform infrared spectroscopy. Results revealed that reacting the graphene oxide PU sponge with the coupling agent titanium(IV)(triethanolaminato)isopropoxide prior to reduction significantly improved oil absorbance. This was due to the improved binding of reduced graphene oxide to the polyurethane framework. The resulting improved rGO-coated PU sponge is a highly efficient and reusable sorbent material and is a promising alternative for oil–water separation applications.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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)0.0000.000

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 head0.106
GPT teacher head0.362
Teacher spread0.255 · 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

Citations89
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicGraphene research and applicationsFrench-language works237,207