MétaCan
Menu
Back to cohort

Highly Hydrophilic Electrospun Polyacrylonitrile/ Polyvinypyrro-lidone Nanofibers Incorporated with Gentamicin as Filter Medium for Dam Water and Wastewater Treatment

2016· article· en· W2500220813 on OpenAlexvenueno aff
Abdulaziz Alharbi, Ibrahim M. Alarifi, Waseem S. Khan, Ramazan Asmatulu

Bibliographic record

VenueJournal of Membrane and Separation Technology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsnot available
FundersOffice of Experimental Program to Stimulate Competitive ResearchKansas NSF EPSCoRWichita State UniversityNational Science Foundation
KeywordsPolyacrylonitrileNanofiberElectrospinningGentamicinWastewaterChemistryPulp and paper industryChemical engineeringMaterials scienceComposite materialEnvironmental scienceEnvironmental engineeringPolymerOrganic chemistryAntibioticsEngineering

Abstract

fetched live from OpenAlex

The need for advancement in filtration technology has spurred attention to advanced materials, such as electrospun nanofiber membranes, for providing clean water at a low cost with minimum initial investment. Polymer nanofibers can be fabricated by using different techniques, such as template synthesis, self-assembly, drawing, phase separation, and electrospinning. Due to its distinctive properties, electrospinning has become a method of choice for fabricating nanofiber membranes quickly with minimal investment. In this study, polyacrylonitrile (PAN) was dissolved in dimethylformamide (DMF), and different weight percentages of polyvinylpyrrolidone (PVP) and gentamicin sulfate powder were added to the solution to fabricate nanomembranes via the electrospinning process. Gentamicin was added to remove bacteria and viruses and prevent fouling, while PVP was added to make the surface of the membrane hydrophilic for enhancing the filtration rate and efficiency. Two water samples were chosen for the filtration processes: dam water and city wastewater. For the dam water sample, PH, turbidity, TDS, Ca++, Mg++, sulfates, nitrates, fluoride, chloride, alkalinity and silica were reduced to +3.64%, 89.6%, 6.52%, 10.5%, 9.96%, 5.16%, 17%, 19.5%, 6.63%, 1.43% and 63.5% respectively. The total coliforms and E. coli content were reduced to 4.1 MPN/100ml and 0 MPN/100ml, respectively with PAN containing 10 wt. % PVP and 5 wt. % Gentamicin. For wastewater sample, PH, turbidity, TDS, TSS, BODs, phosphate, ammonia, oil-greases and DO were reduced to + 3.62%, 79%, 6.33%, 84%, 68%, 1.70%, 15.8%, 0% and 6% respectively. The total coliforms and E. coli content were also lowered to 980 MPN/100ml and 1119.9 MPN/100ml, respectively with PAN containing 10 wt. % PVP and 5 wt. % Gentamicin. The morphology and dimensions of the nanofibers were observed using a scanning electron microscope (SEM). Both SEM and microscopic images of the nanomembrane before and after filtration proved that electrospun PAN nanofibers have superior water filtration performance.

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 score0.001
Threshold uncertainty score0.002

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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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

Citations63
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Membrane and Separation TechnologySame topicElectrospun Nanofibers in Biomedical ApplicationsFrench-language works237,207