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Record W2978787331 · doi:10.1038/s41545-019-0044-z

Carbon-based polymer nanocomposite membranes for oily wastewater treatment

2019· article· en· W2978787331 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenpj Clean Water · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsMembraneCarbon nanotubeNanocompositeMaterials scienceGrapheneBiofoulingCarbon fibersWettingOxideNanotechnologyWastewaterChemical engineeringComposite materialEnvironmental scienceEnvironmental engineeringChemistryComposite numberEngineering

Abstract

fetched live from OpenAlex

Abstract Increasing oil contaminants in water is one of the major environmental concerns due to negative impacts on human health and aquatic and terrestrial ecosystems. The objective of this review paper is to highlight recent advances in the application carbon-based polymer nanocomposite membranes for oily wastewater treatment. Carbon-based nanomaterials, including graphene and graphene-oxide (GO), carbon nanotubes (CNTs), and carbon nanofibers (CNFs), have gained tremendous attention due to their unique physicochemical properties, such as excellent chemical and mechanical stability, electrical conductivity, reinforcement capability, and their antifouling properties. This review encompasses innovative carbon-based membranes for effective oil–water separation and provides a critical comparison of these membranes regarding the permeation flux, wettability, and flux recovery. The current challenges for the successful development of carbon-based nanocomposite membranes and opportunities for future research are also discussed.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.022
Threshold uncertainty score0.998

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)0.0030.002

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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