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Record W4313893986 · doi:10.3390/membranes13010072

Electrospun Silica-Polyacrylonitrile Nanohybrids for Water Treatments

2023· article· en· W4313893986 on OpenAlexafffund
Beata Malczewska, Paweł Lochyński, Sylwia Charazińska, Andrzej Sikora, Ramin Farnood

Bibliographic record

VenueMembranes · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsPolyacrylonitrileHumic acidMembraneNatural organic matterAdsorptionChemical engineeringMaterials scienceContact angleNanofiberPolymerElectrospinningWater treatmentPolymer chemistryChemistryNanotechnologyComposite materialOrganic chemistryWaste management

Abstract

fetched live from OpenAlex

In this work, the removal of NOM (natural organic matter) as represented by humic acid by means of electrospun nanofiber adsorptive membranes (ENAMs) is described. Polyacrylonitrile (PAN) was used for the preparation of ENAMs incorporating silica nanoparticles as adsorbents. The addition of silica to the polymer left visible changes on the structural morphology and fibers' properties of the membrane. The membrane samples were characterized by pure water permeability, contact angle measurement, SEM, XPS, and XRD. This study assesses the preliminary performance of PAN-Si membranes for the removal of natural organic matter (NOM). The membrane rejected the humic acid, a surrogate of NOM, from 69.57% to 87.5%.

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 categoriesInsufficient payload (model declined to judge)
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 score0.073
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)0.0010.003

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 head0.013
GPT teacher head0.270
Teacher spread0.258 · 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.

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

Citations7
Published2023
Admission routes2
Has abstractyes

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