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Record W2900646682 · doi:10.11113/amst.v4i1.43

Polymeric Hollow Fibers: State of the Art Review of Their Preparation, Characterization and Applications in Different Research Areas

2017· article· en· W2900646682 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

VenueJournal of Applied Membrane Science & Technology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDesalinationMembraneCellulose acetateReverse osmosisFabricationMaterials scienceFiberMembrane technologyPhase inversionCharacterization (materials science)Gas separationProcess engineeringNanotechnologyEngineeringComposite materialChemistry

Abstract

fetched live from OpenAlex

In this article an attempt is made to review critically the papers published recently on polymeric hollow fibers and hollow fiber membranes. Hollow fiber membranes emerged in early nineteen sixties at almost the same time as the announcement of the cellulose acetate reverse osmosis membrane for seawater desalination by Loeb and Sourirajan. Since then, the hollow fiber technology has progressed along with the industrial membrane separation processes. Today, hollow fiber membranes are being used in every sector of the manufacturing industry, including gas and vapor separation, seawater desalination and waste water treatment. The fabrication of a hollow fiber membrane with a desirable pore–size distribution and performance is not an easy task. There are many factors controlling fiber morphology during the phase inversion process and, at present, we are not able to say that we fully understand the phenomena involved in the fabrication of hollow fibers. Nevertheless, there has been a large amount of knowledge accumulated during the past fifteen years, which has been supported by an equally large amount of efforts by many researchers. This paper attempts to summarize those works. The authors could however look into only those reports which have appeared in scientific journals and few patents, and they are fully aware that there must be much more information that has not surfaced to the journal publication. It is also the authors’ intention to show the future direction including the research topics that have been studied only little or not at all.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.049
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.020
GPT teacher head0.305
Teacher spread0.285 · 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