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Record W2940598562 · doi:10.1002/pat.4626

Cellulose acetate nanocomposite ultrafiltration membranes tailored with hydrous manganese dioxide nanoparticles for water treatment applications

2019· article· en· W2940598562 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.

Bibliographic record

VenuePolymers for Advanced Technologies · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMembraneUltrafiltration (renal)NanocompositeCellulose acetateMaterials scienceChemical engineeringPermeationContact angleNanoparticleCelluloseFoulingManganeseRegenerated celluloseNuclear chemistryChromatographyChemistryNanotechnologyComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Hydrous manganese dioxide (HMO) nanoparticles incorporated cellulose acetate (CA) composite ultrafiltration (UF) membranes are prepared with the aim of improving the water permeation and BSA contaminant removal. The HMO nanoparticles are synthesized from manganese ion and characterized by FT‐IR, XRD, and FESEM. The effect of variation of HMO on CA membranes is probed using FT‐IR, EDAX, contact angle, SEM, and AFM analysis to demonstrate their chemical functionality, hydrophilicity, and morphology. CA/HMO membranes are showing the enhancement in pure water flux (PWF), water uptake, porosity, hydrophilicity, fouling resistance, BSA rejection, and flux recovery ratio (FRR). CA‐1 membrane displayed higher PWF (143.6 Lm 2 h −1 ), BSA rejection (95.9%), irreversible fouling (93.3%), and FRR (93.3%). Overall results confirmed that the CA/HMO nanocomposite UF membranes overcome the bottlenecks and shows potential for water treatment 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.

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 score0.034
Threshold uncertainty score0.947

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 communication0.0000.001
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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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