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Record W3200415191 · doi:10.1021/acsapm.1c00722

Electrospun Polystyrene and Acid-Treated Cellulose Nanocrystals with Intense Pulsed Light Treatment for N95-Equivalent Filters

2021· article· en· W3200415191 on OpenAlexafffund
Danny Wong, Sean Hartery, Erin Keltie, Rachel Chang, Jong Sung Kim, Simon S. Park

Bibliographic record

VenueACS Applied Polymer Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsDalhousie UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsMaterials scienceFiltration (mathematics)PolystyrenePressure dropRespiratorMembraneElectrospinningCelluloseComposite materialDrop (telecommunication)NanotechnologyChemical engineeringPolymerChemistry

Abstract

fetched live from OpenAlex

The coronavirus (SARS-CoV-2) has prompted a global need and shortage of face masks and respirators. Electrospinning of polystyrene and cellulose nanocrystals (CNCs) is used to fabricate filter materials exceeding N95 standards set by the National Institute for Occupational Health and Safety. The desired filtration efficiency and pressure drop are achieved from filters with varying amounts of input material. The water contact angle, fiber morphology, and surface potential are reported to explain the enhancements in filtration performance after the addition of CNCs or application of intense pulsed light (IPL). The improved durability in the form of increased mechanical properties and lower pressure drop after testing are also examined. The samples exhibited filtration efficiencies greater than 99% and an initial pressure drop as low as 231 Pa. The combination of CNCs and IPL shows the potential to use thinner membranes with less material to achieve comparable filtration performance, which may result in lower manufacturing costs.

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 score0.002
Threshold uncertainty score0.003

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)0.0010.000

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueACS Applied Polymer MaterialsSame topicAerosol Filtration and Electrostatic PrecipitationFrench-language works237,207