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Record W2044420550 · doi:10.1021/ie000443b

A Visualizing Method for Study of Micron Bubble Attachment onto a Solid Surface under Varying Physicochemical Conditions

2000· article· en· W2044420550 on OpenAlexafffund
Chun Yang, Tadeusz Dąbroś, Dongqing Li, Jan Czarnecki, Jacob H. Masliyah

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsSyncrude (Canada)University of AlbertaNatural Resources Canada
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsSyncrude
KeywordsBubbleStagnation pointChemistryJet (fluid)Solid surfaceIonic strengthIonVolumetric flow rateReynolds numberChemical physicsMetalMechanicsAqueous solutionPhysical chemistryHeat transferPhysics

Abstract

fetched live from OpenAlex

The impinging jet technique, a direct microscopic observation method, is presented to fundamentally study micron bubble attachment onto a solid surface (collector) under well-controlled stagnation flow conditions. The bubble−collector attachment near the stagnation point of an impinging jet is analogous to bubble−fine solid interaction, which is of interest to flotation processes. In this work, bubble attachment experiments were conducted for sodium chloride solutions with various concentrations (10 -1 −10 -4 M) and pH values (2.5−9.0) under a fixed flow intensity (in terms of Reynolds number Re = 200). In addition, the effect of metal ion valence on the bubble attachment was examined as well. Results have showed that the bubble attachment flux was dependent on both solution concentration (ionic strength) and pH, suggesting a strong impact of the electrostatic double layer interaction. In the presence of multivalent metal ions, the bubble attachment rate was noticeably enhanced.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 integrity0.0000.001
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 head0.115
GPT teacher head0.451
Teacher spread0.336 · 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

Citations16
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicMinerals Flotation and Separation TechniquesFrench-language works237,207