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Technology Study of Polypropylene Hollow Fiber Membranes-Like Artificial Lung Made by the Melt-Spinning and Cold-Stretching Method

2011· article· en· W2012971398 on OpenAlexaff
Xun Dao Liu, Lei Ni, Yu Feng Zhang, Zhen Liu, Xian She Feng, Lin Ji

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Waterloo
FundersDivision of Materials ResearchTianjin Science and Technology Program
KeywordsSpinningMembraneMaterials scienceHollow fiber membranePolypropyleneAnnealing (glass)Melt spinningArtificial lungComposite materialFiberChemistry

Abstract

fetched live from OpenAlex

Artificial lung also called as oxygenator which performs a function of exchanging O2 and removing CO2 from blood. Due to its good performance at the exchange area, oxygenation, etc, hollow fiber membranes have become the main research direction of artificial lung. Polypropylene (pp) hollow fiber membranes made by the melt-spinning and cold-stretching methods (MSCS) in this study. Through the research on the membrane manufacture process and technology optimization to prepare suitable membrane for artificially lung. The performance of membrane was affected by the melt-draw ratio and spinning temperature, annealing temperature, and the proportional relations of cold stretch with hot stretch. The results of the study show that improve melt-draw ratio, select the appropriate annealing conditions and the reasonable ratio of hot stretch with cold stretch can effectively increase the air flux of pp hollow fiber membrane.

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 communication0.0000.001
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 head0.044
GPT teacher head0.373
Teacher spread0.329 · 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
Published2011
Admission routes1
Has abstractyes

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