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Record W2600649752 · doi:10.1002/cjce.22842

Removal of fine and ultrafine particles by means of a condensational growth assisted bubble column

2017· article· en· W2600649752 on OpenAlexvenueno aff
F. La Motta, Francesco Di Natale, Claudia Carotenuto, Amedeo Lancia

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsnot available
Fundersnot available
KeywordsBubbleCondensationParticle (ecology)PolystyreneTube (container)AerosolParticle sizeMaterials scienceDraft tubeParticle aggregationUltrafine particleChemical engineeringChemistryNanoparticleChromatographyAnalytical Chemistry (journal)NanotechnologyComposite materialMechanicsThermodynamicsPolymerPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The paper shows experimental findings aimed to prove the effectiveness of a concept design for fine and ultrafine particle capture called condensational growth assisted bubble column. Experiments were carried out with a gas at ambient temperature and pressure polluted with calibrated polystyrene nanoparticles (mean numeric diameter 113 nm, standard deviation 120 nm). The laboratory scale equipment included the sequence of a growth tube and a bubble column. In the growth tube, the heterogeneous condensation of water vapour took place over the particles, producing a liquid‐solid aerosol of size larger than the original particles. Experiments showed that the condensational growth pre‐treatment improved the bubble column removal efficiency from nearly 25 % up to 90 %. The growth tube also contributed to particle capture so that the overall system reached particle removal efficiency above 95 %. The particle removal efficiency of the entire unit was higher than the sum of the single growth tube and bubble column contributions, suggesting the occurrence of favourable synergic effect between them.

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 score0.075
Threshold uncertainty score0.236

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 head0.010
GPT teacher head0.192
Teacher spread0.183 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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Same venueThe Canadian Journal of Chemical EngineeringSame topicAerosol Filtration and Electrostatic PrecipitationFrench-language works237,207