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Record W2115295501 · doi:10.11159/ijtan.2012.003

Improvements in the Structure of Electrospun Polyurethane Nanofibrous Materials Used for Bacterial Removal from Wastewater

2012· article· en· W2115295501 on OpenAlexvenueno aff
Jaroslav Lev, Marek Holba, Libor Kalhotka, Přemysl Mikula, Dušan Kimmer

Bibliographic record

VenueInternational Journal of Theoretical and Applied Nanotechnology · 2012
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsnot available
FundersAdvanced Scientific Computing ResearchInstitute of Botany of the Czech Academy of Sciences
KeywordsPolyurethaneWastewaterElectrospinningMaterials sciencePulp and paper industryWaste managementComposite materialPolymerEngineering

Abstract

fetched live from OpenAlex

While the properties of electrospun nanofibrous materials appear promising for filter applications, the ideal material parameters for water filtration are still being investigated.Polyurethane (PU) nanofiber layered on a supporting polypropylene (PP) textile is a good candidate for pathogen removal from water.Here, we show experimentally that pathogen removal from wastewater up to log 4.86 can be achieved using electrospun PU nanofibers.Materials with different area weights (0.3, 0.5, 1.9 and 3.8 g/m 2 ) were used for filtering E. coli from artificial wastewater.Tests were carried

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)0.001
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 score0.062
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.244
Teacher spread0.240 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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