MétaCan
Menu
Back to cohort
Record W2917166107

Effect of Asymmetric Functionalized Graphene Oxide (Janus GO) on Young′s Modulus and Glass Transition Temperature of PSf Ultrafiltration Membrane

2019· article· en· W2917166107 on OpenAlexaff
Mahdi Akbari, Mojtaba Shariaty-Niassar, Takeshi Matsuura, Ahmad Fauzi Ismail

Bibliographic record

VenueInternational journal of nanoscience and nanotechnology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials sciencePolysulfoneMembraneJanusGrapheneUltrafiltration (renal)Glass transitionPhase inversionOxideModulusChemical engineeringThermal stabilitySonicationCastingComposite materialNanotechnologyPolymerChromatographyChemistry
DOInot available

Abstract

fetched live from OpenAlex

In this study, effect of asymmetric functionalized graphene oxide (Janus GO) on Young′s modulus and glass transition temperature of Polysulfone (PSf) ultrafiltration membranes was investigated. The membranes were prepared via phase inversion method and GO nanosheets were dispersed in casting solution by sonication. Results showed that the Normalized Young’s modulus (on the basis of neat PSf membrane Young’s modulus) increased from 1 to 1.35 for neat PSf membrane compared to the membrane with 1% Janus GO nanosheets. This enhancement indicated the improvement of mechanical properties of modified membranes. Also, application of Janus GO nanosheets caused enhancement of thermal stability of modified membranes by increasing glass transition temperature to 182.97 °C compared to 180.1 °C for neat PSf membrane. These improvements were ascribed to the enhancement of dispersion and stability of Janus GO nanosheets in membranes matrix.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.004
GPT teacher head0.228
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational journal of nanoscience and nanotechnologySame topicMembrane Separation TechnologiesFrench-language works237,207