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Record W2739278466 · doi:10.3390/nano7080195

Application of Nanosize Zeolite Molecular Sieves for Medical Oxygen Concentration

2017· article· en· W2739278466 on OpenAlexafffund
Mingfei Pan, Hecham Omar, Sohrab Rohani

Bibliographic record

VenueNanomaterials · 2017
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsPressure swing adsorptionAdsorptionDesorptionAir separationVacuum swing adsorptionMaterials scienceZeoliteMicroporous materialOxygenChemical engineeringVolumetric flow rateMolecular sieveMultiphysicsPartial pressureLimiting oxygen concentrationAnalytical Chemistry (journal)ChemistryChromatographyThermodynamicsComposite materialCatalysisOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The development of a portable oxygen concentrator is of prime significance for patients with respiratory problems. This paper presents a portable concentrator prototype design using the pressure/vacuum swing adsorption (PVSA) cycle with a deep evacuation step (-0.82 barg) instead of desorption with purge flow to simplify the oxygen production process. The output of the oxygen concentrator is a ~90 vol % enriched oxygen stream in a continuous adsorption and desorption cycle (cycle time ~90 s). The size of the adsorption column is 3 cm in diameter and 20 cm in length. A Li⁺ exchanged 13X nanosize zeolite is used as the adsorbent to selectively adsorb nitrogen from air. A dynamic model of the pressure and vacuum swing adsorption units was developed to study the pressurization and depressurization process inside the microporous area of nanosized zeolites. The describing equations were solved using COMSOL Multiphysics Chemical Engineering module. The output flow rate and oxygen concentration results from the simulation model were compared with the experimental data. Velocity and concentration profiles were obtained to study the adsorption process and optimize the operational parameters.

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 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 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 score0.012
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.245
Teacher spread0.239 · 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 teacher head, 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

Citations49
Published2017
Admission routes2
Has abstractyes

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