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Record W6981318016

The Effect of Aligned Porous Nanofibers on Filter Efficiency and Pressure Drop

2023· other· en· W6981318016 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

VenueYork University Digital Library (York University) · 2023
Typeother
Languageen
FieldSocial Sciences
TopicGerman Security and Defense Policies
Canadian institutionsYork University
Fundersnot available
KeywordsElectrospinningPressure dropPorosityFiltration (mathematics)NanofiberMicrofiberFilter (signal processing)Spinning
DOInot available

Abstract

fetched live from OpenAlex

Masks, vital for submicron filtration, face a trade-off between enhanced when increasing filter efficiency and, an increased pressure drop, impacting user comfort during physical activities leading to difficulty breathing. Polycaprolactone (PCL) and Nylon masks electrospun with humidity control, was investigated. Optimal conditions emerged with a 10% PCL solution by weight in a chloroform and dimethylformamide mix (8:2 ratio), collected at 500 RPM, producing highly efficient aligned porous fibers. Conversely, Nylon failed to yield porous fibers under any tested combination of parameters. Our findings reveal a filtration efficiency range for porous aligned PCL fibers from 6% for 0.3 μm particles up to 42% at 5 μm, accompanied by a minimal pressure drop of 7 Pa. Introducing humidity proved effective in manufacturing porous nanofibers within a conventional electrospinning setup, offering promise for exploring diverse materials. The material’s distinct behavior suggests a broad avenue for the development of oriented multilayered mask application.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.106
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.182
Teacher spread0.176 · 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