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Record W1587608095 · doi:10.1002/9781118522318.emst099

Oxygen–Nitrogen Separation

2013· other· en· W1587608095 on OpenAlexaff
Dipak Rana, Takeshi Matsuura

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAir separationMembraneProcess engineeringPolymerOxygenNitrogenMaterials scienceGas separationProcess designChemical engineeringNanotechnologyBiochemical engineeringMechanical engineeringChemistryEngineeringComposite materialOrganic chemistryProcess integration

Abstract

fetched live from OpenAlex

Abstract This article outlines briefly the current status of material design, module design, process and system design, and commercial applications of membrane air separation. In particular, efforts were made to collect the latest experimental data available in the scientific literature. As a result of the literature search, it was found that investigation of various polyimides has been dominating in the material design of polymers, while development of mixed‐matrix membranes has been attempted to combine advantageous features of inorganic and polymeric materials. Module designs are mostly based on hollow fiber and spiral‐wound modules. Economic optimization is the criteria for the process and system design. Applications are mainly in the production of oxygen‐enriched air and nitrogen gas. Production of high purity oxygen from air has not yet been achieved commercially. This review is focused on polymeric and polymer‐related membranes for air separation. Therefore, only limited information is given on inorganic membrane materials that are based on oxygen ion transport mechanism, for example, perovskite.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

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)0.0040.002

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 head0.226
Teacher spread0.217 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2013
Admission routes1
Has abstractyes

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