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Record W3033341327 · doi:10.1002/cjce.23821

Absorption of sulphur dioxide by electrosprayed droplets

2020· article· en· W3033341327 on OpenAlexvenueno aff
Francesco Di Natale, Claudia Carotenuto, Arianna Parisi, Amedeo Lancia

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAbsorption (acoustics)ChemistryCorona dischargeSulfur dioxideScrubberIonCorona (planetary geology)IonizationAnalytical Chemistry (journal)Environmental chemistryInorganic chemistryMaterials scienceElectrodeComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract This paper describes and discusses experimental results on the absorption of sulphur dioxide in electrified water sprays, either when the polluted gas is treated as is or when the gas is exposed to a corona source to ionize the sulphur dioxide. The experiments revealed that an electrified spray with a charge‐to‐mass ratio of 50 μC · kg−1 enabled the absorption rate of droplets to double, regardless of their polarities. Corona charging gave rise to an increase in the SO2 depletion rate over the scrubber wall, while negligible effects appeared on the actual droplets absorption rate. These findings suggested that faster absorption rates mostly, though not uniquely, depend on the modifications on the morphological and interfacial properties of the sprayed droplets induced by the free electric charge imposed on their surface. Conversely, the absorption rates were negligibly affected by the electrical interactions between droplets (either charged or uncharged) and the sulphur dioxide ions/radicals originating from the corona source.

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 score0.005

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.153
Teacher spread0.150 · 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

Citations21
Published2020
Admission routes1
Has abstractyes

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